Guidelines

Guidelines#

Naming conventions#

hipBLAS follows the following naming conventions,

Big case for matrix, e.g. matrix A, B, C GEMM (C = A*B)
Lower case for vector, e.g. vector x, y GEMV (y = A*x)

Notations#

hipBLAS function uses the following notations to denote precisions,

h = half
bf = 16 bit brain floating point
s = single
d = double
c = single complex
z = double complex

hipBLAS Types#

Definitions#

hipblasHandle_t#

typedef void *hipblasHandle_t#: hipblasHanlde_t is a void pointer, to store the library context (either rocBLAS or cuBLAS)

hipblasHalf#

typedef uint16_t hipblasHalf#: To specify the datatype to be signed short.

hipblasInt8#

typedef int8_t hipblasInt8#: To specify the datatype to be signed char.

hipblasStride#

typedef int64_t hipblasStride#: Stride between matrices or vectors in strided_batched functions.

hipblasBfloat16#

struct hipblasBfloat16#: Struct to represent a 16 bit Brain floating-point number.

hipblasComplex#

struct hipblasComplex#: Struct to represent a complex number with single precision real and imaginary parts.

hipblasDoubleComplex#

struct hipblasDoubleComplex#: Struct to represent a complex number with double precision real and imaginary parts.

Enums#

Enumeration constants have numbering that is consistent with CBLAS, ACML and most standard C BLAS libraries.

hipblasStatus_t#

enum hipblasStatus_t#

hipblas status codes definition

Values:

enumerator HIPBLAS_STATUS_SUCCESS#: Function succeeds

enumerator HIPBLAS_STATUS_NOT_INITIALIZED#: HIPBLAS library not initialized

enumerator HIPBLAS_STATUS_ALLOC_FAILED#: resource allocation failed

enumerator HIPBLAS_STATUS_INVALID_VALUE#: unsupported numerical value was passed to function

enumerator HIPBLAS_STATUS_MAPPING_ERROR#: access to GPU memory space failed

enumerator HIPBLAS_STATUS_EXECUTION_FAILED#: GPU program failed to execute

enumerator HIPBLAS_STATUS_INTERNAL_ERROR#: an internal HIPBLAS operation failed

enumerator HIPBLAS_STATUS_NOT_SUPPORTED#: function not implemented

enumerator HIPBLAS_STATUS_ARCH_MISMATCH#: architecture mismatch

enumerator HIPBLAS_STATUS_HANDLE_IS_NULLPTR#: hipBLAS handle is null pointer

enumerator HIPBLAS_STATUS_INVALID_ENUM#: unsupported enum value was passed to function

enumerator HIPBLAS_STATUS_UNKNOWN#: back-end returned an unsupported status code

hipblasOperation_t#

enum hipblasOperation_t#

Used to specify whether the matrix is to be transposed or not.

Values:

enumerator HIPBLAS_OP_N#: Operate with the matrix.

enumerator HIPBLAS_OP_T#: Operate with the transpose of the matrix.

enumerator HIPBLAS_OP_C#: Operate with the conjugate transpose of the matrix.

hipblasPointerMode_t#

enum hipblasPointerMode_t#

Indicates if scalar pointers are on host or device. This is used for scalars alpha and beta and for scalar function return values.

Values:

enumerator HIPBLAS_POINTER_MODE_HOST#: Scalar values affected by this variable will be located on the host.

enumerator HIPBLAS_POINTER_MODE_DEVICE#: Scalar values affected by this variable will be located on the device.

hipblasFillMode_t#

enum hipblasFillMode_t#

Used by the Hermitian, symmetric and triangular matrix routines to specify whether the upper or lower triangle is being referenced.

Values:

enumerator HIPBLAS_FILL_MODE_UPPER#: Upper triangle

enumerator HIPBLAS_FILL_MODE_LOWER#: Lower triangle

enumerator HIPBLAS_FILL_MODE_FULL#

hipblasDiagType_t#

enum hipblasDiagType_t#

It is used by the triangular matrix routines to specify whether the matrix is unit triangular.

Values:

enumerator HIPBLAS_DIAG_NON_UNIT#: Non-unit triangular.

enumerator HIPBLAS_DIAG_UNIT#: Unit triangular.

hipblasSideMode_t#

enum hipblasSideMode_t#

Indicates the side matrix A is located relative to matrix B during multiplication.

Values:

enumerator HIPBLAS_SIDE_LEFT#: Multiply general matrix by symmetric, Hermitian or triangular matrix on the left.

enumerator HIPBLAS_SIDE_RIGHT#: Multiply general matrix by symmetric, Hermitian or triangular matrix on the right.

enumerator HIPBLAS_SIDE_BOTH#

hipblasDatatype_t#

enum hipblasDatatype_t#

Indicates the precision width of data stored in a blas type.

Values:

enumerator HIPBLAS_R_16F#: 16 bit floating point, real

enumerator HIPBLAS_R_32F#: 32 bit floating point, real

enumerator HIPBLAS_R_64F#: 64 bit floating point, real

enumerator HIPBLAS_C_16F#: 16 bit floating point, complex

enumerator HIPBLAS_C_32F#: 32 bit floating point, complex

enumerator HIPBLAS_C_64F#: 64 bit floating point, complex

enumerator HIPBLAS_R_8I#: 8 bit signed integer, real

enumerator HIPBLAS_R_8U#: 8 bit unsigned integer, real

enumerator HIPBLAS_R_32I#: 32 bit signed integer, real

enumerator HIPBLAS_R_32U#: 32 bit unsigned integer, real

enumerator HIPBLAS_C_8I#: 8 bit signed integer, complex

enumerator HIPBLAS_C_8U#: 8 bit unsigned integer, complex

enumerator HIPBLAS_C_32I#: 32 bit signed integer, complex

enumerator HIPBLAS_C_32U#: 32 bit unsigned integer, complex

enumerator HIPBLAS_R_16B#: 16 bit bfloat, real

enumerator HIPBLAS_C_16B#: 16 bit bfloat, complex

hipblasGemmAlgo_t#

enum hipblasGemmAlgo_t#

Indicates if layer is active with bitmask.

Values:

enumerator HIPBLAS_GEMM_DEFAULT#: enumerator rocblas_gemm_algo_standard

hipblasAtomicsMode_t#

enum hipblasAtomicsMode_t#

Indicates if atomics operations are allowed. Not allowing atomic operations may generally improve determinism and repeatability of results at a cost of performance.

Values:

enumerator HIPBLAS_ATOMICS_NOT_ALLOWED#: Algorithms will refrain from atomics where applicable.

enumerator HIPBLAS_ATOMICS_ALLOWED#: Algorithms will take advantage of atomics where applicable.

hipBLAS Functions#

Level 1 BLAS#

hipblasIXamax + Batched, StridedBatched #

hipblasStatus_t hipblasIsamax(hipblasHandle_t handle, int n, const float *x, int incx, int *result)#

hipblasStatus_t hipblasIdamax(hipblasHandle_t handle, int n, const double *x, int incx, int *result)#

hipblasStatus_t hipblasIcamax(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, int *result)#

hipblasStatus_t hipblasIzamax(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, int *result)#

BLAS Level 1 API.

amax finds the first index of the element of maximum magnitude of a vector x.

Supported precisions in rocBLAS : s,d,c,z.
Supported precisions in cuBLAS : s,d,c,z.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of y.
result – [inout] device pointer or host pointer to store the amax index. return is 0.0 if n, incx<=0.

hipblasStatus_t hipblasIsamaxBatched(hipblasHandle_t handle, int n, const float *const x[], int incx, int batchCount, int *result)#

hipblasStatus_t hipblasIdamaxBatched(hipblasHandle_t handle, int n, const double *const x[], int incx, int batchCount, int *result)#

hipblasStatus_t hipblasIcamaxBatched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, int batchCount, int *result)#

hipblasStatus_t hipblasIzamaxBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *const x[], int incx, int batchCount, int *result)#

BLAS Level 1 API.

amaxBatched finds the first index of the element of maximum magnitude of each vector x_i in a batch, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z.
Supported precisions in cuBLAS : No support.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each vector x_i
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
batchCount – [in] [int] number of instances in the batch, must be > 0.
result – [out] device or host array of pointers of batchCount size for results. return is 0 if n, incx<=0.

hipblasStatus_t hipblasIsamaxStridedBatched(hipblasHandle_t handle, int n, const float *x, int incx, hipblasStride stridex, int batchCount, int *result)#

hipblasStatus_t hipblasIdamaxStridedBatched(hipblasHandle_t handle, int n, const double *x, int incx, hipblasStride stridex, int batchCount, int *result)#

hipblasStatus_t hipblasIcamaxStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, int batchCount, int *result)#

hipblasStatus_t hipblasIzamaxStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount, int *result)#

BLAS Level 1 API.

amaxStridedBatched finds the first index of the element of maximum magnitude of each vector x_i in a batch, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each vector x_i
x – [in] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
stridex – [in] [hipblasStride] specifies the pointer increment between one x_i and the next x_(i + 1).
batchCount – [in] [int] number of instances in the batch
result – [out] device or host pointer for storing contiguous batchCount results. return is 0 if n <= 0, incx<=0.

hipblasIXamin + Batched, StridedBatched #

hipblasStatus_t hipblasIsamin(hipblasHandle_t handle, int n, const float *x, int incx, int *result)#

hipblasStatus_t hipblasIdamin(hipblasHandle_t handle, int n, const double *x, int incx, int *result)#

hipblasStatus_t hipblasIcamin(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, int *result)#

hipblasStatus_t hipblasIzamin(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, int *result)#

BLAS Level 1 API.

amin finds the first index of the element of minimum magnitude of a vector x.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of y.
result – [inout] device pointer or host pointer to store the amin index. return is 0.0 if n, incx<=0.

hipblasStatus_t hipblasIsaminBatched(hipblasHandle_t handle, int n, const float *const x[], int incx, int batchCount, int *result)#

hipblasStatus_t hipblasIdaminBatched(hipblasHandle_t handle, int n, const double *const x[], int incx, int batchCount, int *result)#

hipblasStatus_t hipblasIcaminBatched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, int batchCount, int *result)#

hipblasStatus_t hipblasIzaminBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *const x[], int incx, int batchCount, int *result)#

BLAS Level 1 API.

aminBatched finds the first index of the element of minimum magnitude of each vector x_i in a batch, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each vector x_i
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
batchCount – [in] [int] number of instances in the batch, must be > 0.
result – [out] device or host pointers to array of batchCount size for results. return is 0 if n, incx<=0.

hipblasStatus_t hipblasIsaminStridedBatched(hipblasHandle_t handle, int n, const float *x, int incx, hipblasStride stridex, int batchCount, int *result)#

hipblasStatus_t hipblasIdaminStridedBatched(hipblasHandle_t handle, int n, const double *x, int incx, hipblasStride stridex, int batchCount, int *result)#

hipblasStatus_t hipblasIcaminStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, int batchCount, int *result)#

hipblasStatus_t hipblasIzaminStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount, int *result)#

BLAS Level 1 API.

aminStridedBatched finds the first index of the element of minimum magnitude of each vector x_i in a batch, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each vector x_i
x – [in] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
stridex – [in] [hipblasStride] specifies the pointer increment between one x_i and the next x_(i + 1)
batchCount – [in] [int] number of instances in the batch
result – [out] device or host pointer to array for storing contiguous batchCount results. return is 0 if n <= 0, incx<=0.

hipblasXasum + Batched, StridedBatched #

hipblasStatus_t hipblasSasum(hipblasHandle_t handle, int n, const float *x, int incx, float *result)#

hipblasStatus_t hipblasDasum(hipblasHandle_t handle, int n, const double *x, int incx, double *result)#

hipblasStatus_t hipblasScasum(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, float *result)#

hipblasStatus_t hipblasDzasum(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, double *result)#

BLAS Level 1 API.

asum computes the sum of the magnitudes of elements of a real vector x, or the sum of magnitudes of the real and imaginary parts of elements if x is a complex vector.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x. incx must be > 0.
result – [inout] device pointer or host pointer to store the asum product. return is 0.0 if n <= 0.

hipblasStatus_t hipblasSasumBatched(hipblasHandle_t handle, int n, const float *const x[], int incx, int batchCount, float *result)#

hipblasStatus_t hipblasDasumBatched(hipblasHandle_t handle, int n, const double *const x[], int incx, int batchCount, double *result)#

hipblasStatus_t hipblasScasumBatched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, int batchCount, float *result)#

hipblasStatus_t hipblasDzasumBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *const x[], int incx, int batchCount, double *result)#

BLAS Level 1 API.

asumBatched computes the sum of the magnitudes of the elements in a batch of real vectors x_i, or the sum of magnitudes of the real and imaginary parts of elements if x_i is a complex vector, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each vector x_i
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
batchCount – [in] [int] number of instances in the batch.
result – [out] device array or host array of batchCount size for results. return is 0.0 if n, incx<=0.

hipblasStatus_t hipblasSasumStridedBatched(hipblasHandle_t handle, int n, const float *x, int incx, hipblasStride stridex, int batchCount, float *result)#

hipblasStatus_t hipblasDasumStridedBatched(hipblasHandle_t handle, int n, const double *x, int incx, hipblasStride stridex, int batchCount, double *result)#

hipblasStatus_t hipblasScasumStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, int batchCount, float *result)#

hipblasStatus_t hipblasDzasumStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount, double *result)#

BLAS Level 1 API.

asumStridedBatched computes the sum of the magnitudes of elements of a real vectors x_i, or the sum of magnitudes of the real and imaginary parts of elements if x_i is a complex vector, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each vector x_i
x – [in] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stride_x, however the user should take care to ensure that stride_x is of appropriate size, for a typical case this means stride_x >= n * incx.
batchCount – [in] [int] number of instances in the batch
result – [out] device pointer or host pointer to array for storing contiguous batchCount results. return is 0.0 if n, incx<=0.

hipblasXaxpy + Batched, StridedBatched #

hipblasStatus_t hipblasHaxpy(hipblasHandle_t handle, int n, const hipblasHalf *alpha, const hipblasHalf *x, int incx, hipblasHalf *y, int incy)#

hipblasStatus_t hipblasSaxpy(hipblasHandle_t handle, int n, const float *alpha, const float *x, int incx, float *y, int incy)#

hipblasStatus_t hipblasDaxpy(hipblasHandle_t handle, int n, const double *alpha, const double *x, int incx, double *y, int incy)#

hipblasStatus_t hipblasCaxpy(hipblasHandle_t handle, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZaxpy(hipblasHandle_t handle, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *y, int incy)#

BLAS Level 1 API.

axpy computes constant alpha multiplied by vector x, plus vector y

y := alpha * x + y

Supported precisions in rocBLAS : h,s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
alpha – [in] device pointer or host pointer to specify the scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [out] device pointer storing vector y.
incy – [inout] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasHaxpyBatched(hipblasHandle_t handle, int n, const hipblasHalf *alpha, const hipblasHalf *const x[], int incx, hipblasHalf *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasSaxpyBatched(hipblasHandle_t handle, int n, const float *alpha, const float *const x[], int incx, float *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasDaxpyBatched(hipblasHandle_t handle, int n, const double *alpha, const double *const x[], int incx, double *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasCaxpyBatched(hipblasHandle_t handle, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, hipblasComplex *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasZaxpyBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const y[], int incy, int batchCount)#

BLAS Level 1 API.

axpyBatched compute y := alpha * x + y over a set of batched vectors.

Supported precisions in rocBLAS : h,s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
alpha – [in] specifies the scalar alpha.
x – [in] pointer storing vector x on the GPU.
incx – [in] [int] specifies the increment for the elements of x.
y – [out] pointer storing vector y on the GPU.
incy – [inout] [int] specifies the increment for the elements of y.
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasHaxpyStridedBatched(hipblasHandle_t handle, int n, const hipblasHalf *alpha, const hipblasHalf *x, int incx, hipblasStride stridex, hipblasHalf *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasSaxpyStridedBatched(hipblasHandle_t handle, int n, const float *alpha, const float *x, int incx, hipblasStride stridex, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDaxpyStridedBatched(hipblasHandle_t handle, int n, const double *alpha, const double *x, int incx, hipblasStride stridex, double *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasCaxpyStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZaxpyStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 1 API.

axpyStridedBatched compute y := alpha * x + y over a set of strided batched vectors.

Supported precisions in rocBLAS : h,s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int]
alpha – [in] specifies the scalar alpha.
x – [in] pointer storing vector x on the GPU.
incx – [in] [int] specifies the increment for the elements of x.
stridex – [in] [hipblasStride] specifies the increment between vectors of x.
y – [out] pointer storing vector y on the GPU.
incy – [inout] [int] specifies the increment for the elements of y.
stridey – [in] [hipblasStride] specifies the increment between vectors of y.
batchCount – [in] [int] number of instances in the batch

hipblasXcopy + Batched, StridedBatched #

hipblasStatus_t hipblasScopy(hipblasHandle_t handle, int n, const float *x, int incx, float *y, int incy)#

hipblasStatus_t hipblasDcopy(hipblasHandle_t handle, int n, const double *x, int incx, double *y, int incy)#

hipblasStatus_t hipblasCcopy(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZcopy(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *y, int incy)#

BLAS Level 1 API.

copy copies each element x[i] into y[i], for i = 1 , … , n

y := x,

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x to be copied to y.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [out] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasScopyBatched(hipblasHandle_t handle, int n, const float *const x[], int incx, float *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasDcopyBatched(hipblasHandle_t handle, int n, const double *const x[], int incx, double *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasCcopyBatched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, hipblasComplex *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasZcopyBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const y[], int incy, int batchCount)#

BLAS Level 1 API.

copyBatched copies each element x_i[j] into y_i[j], for j = 1 , … , n; i = 1 , … , batchCount

y_i := x_i,

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i to be copied to y_i.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each vector x_i.
y – [out] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each vector y_i.
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasScopyStridedBatched(hipblasHandle_t handle, int n, const float *x, int incx, hipblasStride stridex, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDcopyStridedBatched(hipblasHandle_t handle, int n, const double *x, int incx, hipblasStride stridex, double *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasCcopyStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZcopyStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 1 API.

copyStridedBatched copies each element x_i[j] into y_i[j], for j = 1 , … , n; i = 1 , … , batchCount

y_i := x_i,

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i to be copied to y_i.
x – [in] device pointer to the first vector (x_1) in the batch.
incx – [in] [int] specifies the increments for the elements of vectors x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stride_x, however the user should take care to ensure that stride_x is of appropriate size, for a typical case this means stride_x >= n * incx.
y – [out] device pointer to the first vector (y_1) in the batch.
incy – [in] [int] specifies the increment for the elements of vectors y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1). There are no restrictions placed on stride_y, however the user should take care to ensure that stride_y is of appropriate size, for a typical case this means stride_y >= n * incy. stridey should be non zero.
batchCount – [in] [int] number of instances in the batch

hipblasXdot + Batched, StridedBatched #

hipblasStatus_t hipblasHdot(hipblasHandle_t handle, int n, const hipblasHalf *x, int incx, const hipblasHalf *y, int incy, hipblasHalf *result)#

hipblasStatus_t hipblasBfdot(hipblasHandle_t handle, int n, const hipblasBfloat16 *x, int incx, const hipblasBfloat16 *y, int incy, hipblasBfloat16 *result)#

hipblasStatus_t hipblasSdot(hipblasHandle_t handle, int n, const float *x, int incx, const float *y, int incy, float *result)#

hipblasStatus_t hipblasDdot(hipblasHandle_t handle, int n, const double *x, int incx, const double *y, int incy, double *result)#

hipblasStatus_t hipblasCdotc(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, const hipblasComplex *y, int incy, hipblasComplex *result)#

hipblasStatus_t hipblasCdotu(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, const hipblasComplex *y, int incy, hipblasComplex *result)#

hipblasStatus_t hipblasZdotc(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *y, int incy, hipblasDoubleComplex *result)#

hipblasStatus_t hipblasZdotu(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *y, int incy, hipblasDoubleComplex *result)#

BLAS Level 1 API.

dot(u) performs the dot product of vectors x and y

result = x * y;

dotc performs the dot product of the conjugate of complex vector x and complex vector y

result = conjugate (x) * y;

Supported precisions in rocBLAS : h,bf,s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of y.
y – [in] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.
result – [inout] device pointer or host pointer to store the dot product. return is 0.0 if n <= 0.

hipblasStatus_t hipblasHdotBatched(hipblasHandle_t handle, int n, const hipblasHalf *const x[], int incx, const hipblasHalf *const y[], int incy, int batchCount, hipblasHalf *result)#

hipblasStatus_t hipblasBfdotBatched(hipblasHandle_t handle, int n, const hipblasBfloat16 *const x[], int incx, const hipblasBfloat16 *const y[], int incy, int batchCount, hipblasBfloat16 *result)#

hipblasStatus_t hipblasSdotBatched(hipblasHandle_t handle, int n, const float *const x[], int incx, const float *const y[], int incy, int batchCount, float *result)#

hipblasStatus_t hipblasDdotBatched(hipblasHandle_t handle, int n, const double *const x[], int incx, const double *const y[], int incy, int batchCount, double *result)#

hipblasStatus_t hipblasCdotcBatched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, const hipblasComplex *const y[], int incy, int batchCount, hipblasComplex *result)#

hipblasStatus_t hipblasCdotuBatched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, const hipblasComplex *const y[], int incy, int batchCount, hipblasComplex *result)#

hipblasStatus_t hipblasZdotcBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *const y[], int incy, int batchCount, hipblasDoubleComplex *result)#

hipblasStatus_t hipblasZdotuBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *const y[], int incy, int batchCount, hipblasDoubleComplex *result)#

BLAS Level 1 API.

dotBatched(u) performs a batch of dot products of vectors x and y

result_i = x_i * y_i;

dotcBatched performs a batch of dot products of the conjugate of complex vector x and complex vector y

result_i = conjugate (x_i) * y_i;

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, …, batchCount

Supported precisions in rocBLAS : h,bf,s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [in] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
batchCount – [in] [int] number of instances in the batch
result – [inout] device array or host array of batchCount size to store the dot products of each batch. return 0.0 for each element if n <= 0.

hipblasStatus_t hipblasHdotStridedBatched(hipblasHandle_t handle, int n, const hipblasHalf *x, int incx, hipblasStride stridex, const hipblasHalf *y, int incy, hipblasStride stridey, int batchCount, hipblasHalf *result)#

hipblasStatus_t hipblasBfdotStridedBatched(hipblasHandle_t handle, int n, const hipblasBfloat16 *x, int incx, hipblasStride stridex, const hipblasBfloat16 *y, int incy, hipblasStride stridey, int batchCount, hipblasBfloat16 *result)#

hipblasStatus_t hipblasSdotStridedBatched(hipblasHandle_t handle, int n, const float *x, int incx, hipblasStride stridex, const float *y, int incy, hipblasStride stridey, int batchCount, float *result)#

hipblasStatus_t hipblasDdotStridedBatched(hipblasHandle_t handle, int n, const double *x, int incx, hipblasStride stridex, const double *y, int incy, hipblasStride stridey, int batchCount, double *result)#

hipblasStatus_t hipblasCdotcStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *y, int incy, hipblasStride stridey, int batchCount, hipblasComplex *result)#

hipblasStatus_t hipblasCdotuStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *y, int incy, hipblasStride stridey, int batchCount, hipblasComplex *result)#

hipblasStatus_t hipblasZdotcStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount, hipblasDoubleComplex *result)#

hipblasStatus_t hipblasZdotuStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount, hipblasDoubleComplex *result)#

BLAS Level 1 API.

dotStridedBatched(u) performs a batch of dot products of vectors x and y

result_i = x_i * y_i;

dotcStridedBatched performs a batch of dot products of the conjugate of complex vector x and complex vector y

result_i = conjugate (x_i) * y_i;

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, …, batchCount

Supported precisions in rocBLAS : h,bf,s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [in] device pointer to the first vector (x_1) in the batch.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1)
y – [in] device pointer to the first vector (y_1) in the batch.
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1)
batchCount – [in] [int] number of instances in the batch
result – [inout] device array or host array of batchCount size to store the dot products of each batch. return 0.0 for each element if n <= 0.

hipblasXnrm2 + Batched, StridedBatched #

hipblasStatus_t hipblasSnrm2(hipblasHandle_t handle, int n, const float *x, int incx, float *result)#

hipblasStatus_t hipblasDnrm2(hipblasHandle_t handle, int n, const double *x, int incx, double *result)#

hipblasStatus_t hipblasScnrm2(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, float *result)#

hipblasStatus_t hipblasDznrm2(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, double *result)#

BLAS Level 1 API.

nrm2 computes the euclidean norm of a real or complex vector

      result := sqrt( x'*x ) for real vectors
      result := sqrt( x**H*x ) for complex vectors

Supported precisions in rocBLAS : s,d,c,z,sc,dz
Supported precisions in cuBLAS : s,d,sc,dz

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of y.
result – [inout] device pointer or host pointer to store the nrm2 product. return is 0.0 if n, incx<=0.

hipblasStatus_t hipblasSnrm2Batched(hipblasHandle_t handle, int n, const float *const x[], int incx, int batchCount, float *result)#

hipblasStatus_t hipblasDnrm2Batched(hipblasHandle_t handle, int n, const double *const x[], int incx, int batchCount, double *result)#

hipblasStatus_t hipblasScnrm2Batched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, int batchCount, float *result)#

hipblasStatus_t hipblasDznrm2Batched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *const x[], int incx, int batchCount, double *result)#

BLAS Level 1 API.

nrm2Batched computes the euclidean norm over a batch of real or complex vectors

      result := sqrt( x_i'*x_i ) for real vectors x, for i = 1, ..., batchCount
      result := sqrt( x_i**H*x_i ) for complex vectors x, for i = 1, ..., batchCount

Supported precisions in rocBLAS : s,d,c,z,sc,dz
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
batchCount – [in] [int] number of instances in the batch
result – [out] device pointer or host pointer to array of batchCount size for nrm2 results. return is 0.0 for each element if n <= 0, incx<=0.

hipblasStatus_t hipblasSnrm2StridedBatched(hipblasHandle_t handle, int n, const float *x, int incx, hipblasStride stridex, int batchCount, float *result)#

hipblasStatus_t hipblasDnrm2StridedBatched(hipblasHandle_t handle, int n, const double *x, int incx, hipblasStride stridex, int batchCount, double *result)#

hipblasStatus_t hipblasScnrm2StridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, int batchCount, float *result)#

hipblasStatus_t hipblasDznrm2StridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount, double *result)#

BLAS Level 1 API.

nrm2StridedBatched computes the euclidean norm over a batch of real or complex vectors

      := sqrt( x_i'*x_i ) for real vectors x, for i = 1, ..., batchCount
      := sqrt( x_i**H*x_i ) for complex vectors, for i = 1, ..., batchCount

Supported precisions in rocBLAS : s,d,c,z,sc,dz
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i.
x – [in] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stride_x, however the user should take care to ensure that stride_x is of appropriate size, for a typical case this means stride_x >= n * incx.
batchCount – [in] [int] number of instances in the batch
result – [out] device pointer or host pointer to array for storing contiguous batchCount results. return is 0.0 for each element if n <= 0, incx<=0.

hipblasXrot + Batched, StridedBatched #

hipblasStatus_t hipblasSrot(hipblasHandle_t handle, int n, float *x, int incx, float *y, int incy, const float *c, const float *s)#

hipblasStatus_t hipblasDrot(hipblasHandle_t handle, int n, double *x, int incx, double *y, int incy, const double *c, const double *s)#

hipblasStatus_t hipblasCrot(hipblasHandle_t handle, int n, hipblasComplex *x, int incx, hipblasComplex *y, int incy, const float *c, const hipblasComplex *s)#

hipblasStatus_t hipblasCsrot(hipblasHandle_t handle, int n, hipblasComplex *x, int incx, hipblasComplex *y, int incy, const float *c, const float *s)#

hipblasStatus_t hipblasZrot(hipblasHandle_t handle, int n, hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *y, int incy, const double *c, const hipblasDoubleComplex *s)#

hipblasStatus_t hipblasZdrot(hipblasHandle_t handle, int n, hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *y, int incy, const double *c, const double *s)#

BLAS Level 1 API.

rot applies the Givens rotation matrix defined by c=cos(alpha) and s=sin(alpha) to vectors x and y. Scalars c and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

Supported precisions in rocBLAS : s,d,c,z,sc,dz
Supported precisions in cuBLAS : s,d,c,z,cs,zd

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in the x and y vectors.
x – [inout] device pointer storing vector x.
incx – [in] [int] specifies the increment between elements of x.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment between elements of y.
c – [in] device pointer or host pointer storing scalar cosine component of the rotation matrix.
s – [in] device pointer or host pointer storing scalar sine component of the rotation matrix.

hipblasStatus_t hipblasSrotBatched(hipblasHandle_t handle, int n, float *const x[], int incx, float *const y[], int incy, const float *c, const float *s, int batchCount)#

hipblasStatus_t hipblasDrotBatched(hipblasHandle_t handle, int n, double *const x[], int incx, double *const y[], int incy, const double *c, const double *s, int batchCount)#

hipblasStatus_t hipblasCrotBatched(hipblasHandle_t handle, int n, hipblasComplex *const x[], int incx, hipblasComplex *const y[], int incy, const float *c, const hipblasComplex *s, int batchCount)#

hipblasStatus_t hipblasCsrotBatched(hipblasHandle_t handle, int n, hipblasComplex *const x[], int incx, hipblasComplex *const y[], int incy, const float *c, const float *s, int batchCount)#

hipblasStatus_t hipblasZrotBatched(hipblasHandle_t handle, int n, hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const y[], int incy, const double *c, const hipblasDoubleComplex *s, int batchCount)#

hipblasStatus_t hipblasZdrotBatched(hipblasHandle_t handle, int n, hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const y[], int incy, const double *c, const double *s, int batchCount)#

BLAS Level 1 API.

rotBatched applies the Givens rotation matrix defined by c=cos(alpha) and s=sin(alpha) to batched vectors x_i and y_i, for i = 1, …, batchCount. Scalars c and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

Supported precisions in rocBLAS : s,d,sc,dz
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i and y_i vectors.
x – [inout] device array of deivce pointers storing each vector x_i.
incx – [in] [int] specifies the increment between elements of each x_i.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment between elements of each y_i.
c – [in] device pointer or host pointer to scalar cosine component of the rotation matrix.
s – [in] device pointer or host pointer to scalar sine component of the rotation matrix.
batchCount – [in] [int] the number of x and y arrays, i.e. the number of batches.

hipblasStatus_t hipblasSrotStridedBatched(hipblasHandle_t handle, int n, float *x, int incx, hipblasStride stridex, float *y, int incy, hipblasStride stridey, const float *c, const float *s, int batchCount)#

hipblasStatus_t hipblasDrotStridedBatched(hipblasHandle_t handle, int n, double *x, int incx, hipblasStride stridex, double *y, int incy, hipblasStride stridey, const double *c, const double *s, int batchCount)#

hipblasStatus_t hipblasCrotStridedBatched(hipblasHandle_t handle, int n, hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *y, int incy, hipblasStride stridey, const float *c, const hipblasComplex *s, int batchCount)#

hipblasStatus_t hipblasCsrotStridedBatched(hipblasHandle_t handle, int n, hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *y, int incy, hipblasStride stridey, const float *c, const float *s, int batchCount)#

hipblasStatus_t hipblasZrotStridedBatched(hipblasHandle_t handle, int n, hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *y, int incy, hipblasStride stridey, const double *c, const hipblasDoubleComplex *s, int batchCount)#

hipblasStatus_t hipblasZdrotStridedBatched(hipblasHandle_t handle, int n, hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *y, int incy, hipblasStride stridey, const double *c, const double *s, int batchCount)#

BLAS Level 1 API.

rotStridedBatched applies the Givens rotation matrix defined by c=cos(alpha) and s=sin(alpha) to strided batched vectors x_i and y_i, for i = 1, …, batchCount. Scalars c and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

Supported precisions in rocBLAS : s,d,sc,dz
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i and y_i vectors.
x – [inout] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment between elements of each x_i.
stridex – [in] [hipblasStride] specifies the increment from the beginning of x_i to the beginning of x_(i+1)
y – [inout] device pointer to the first vector y_1.
incy – [in] [int] specifies the increment between elements of each y_i.
stridey – [in] [hipblasStride] specifies the increment from the beginning of y_i to the beginning of y_(i+1)
c – [in] device pointer or host pointer to scalar cosine component of the rotation matrix.
s – [in] device pointer or host pointer to scalar sine component of the rotation matrix.
batchCount – [in] [int] the number of x and y arrays, i.e. the number of batches.

hipblasXrotg + Batched, StridedBatched #

hipblasStatus_t hipblasSrotg(hipblasHandle_t handle, float *a, float *b, float *c, float *s)#

hipblasStatus_t hipblasDrotg(hipblasHandle_t handle, double *a, double *b, double *c, double *s)#

hipblasStatus_t hipblasCrotg(hipblasHandle_t handle, hipblasComplex *a, hipblasComplex *b, float *c, hipblasComplex *s)#

hipblasStatus_t hipblasZrotg(hipblasHandle_t handle, hipblasDoubleComplex *a, hipblasDoubleComplex *b, double *c, hipblasDoubleComplex *s)#

BLAS Level 1 API.

rotg creates the Givens rotation matrix for the vector (a b). Scalars c and s and arrays a and b may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode. If the pointer mode is set to HIPBLAS_POINTER_MODE_HOST, this function blocks the CPU until the GPU has finished and the results are available in host memory. If the pointer mode is set to HIPBLAS_POINTER_MODE_DEVICE, this function returns immediately and synchronization is required to read the results.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
a – [inout] device pointer or host pointer to input vector element, overwritten with r.
b – [inout] device pointer or host pointer to input vector element, overwritten with z.
c – [inout] device pointer or host pointer to cosine element of Givens rotation.
s – [inout] device pointer or host pointer sine element of Givens rotation.

hipblasStatus_t hipblasSrotgBatched(hipblasHandle_t handle, float *const a[], float *const b[], float *const c[], float *const s[], int batchCount)#

hipblasStatus_t hipblasDrotgBatched(hipblasHandle_t handle, double *const a[], double *const b[], double *const c[], double *const s[], int batchCount)#

hipblasStatus_t hipblasCrotgBatched(hipblasHandle_t handle, hipblasComplex *const a[], hipblasComplex *const b[], float *const c[], hipblasComplex *const s[], int batchCount)#

hipblasStatus_t hipblasZrotgBatched(hipblasHandle_t handle, hipblasDoubleComplex *const a[], hipblasDoubleComplex *const b[], double *const c[], hipblasDoubleComplex *const s[], int batchCount)#

BLAS Level 1 API.

rotgBatched creates the Givens rotation matrix for the batched vectors (a_i b_i), for i = 1, …, batchCount. a, b, c, and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode. If the pointer mode is set to HIPBLAS_POINTER_MODE_HOST, this function blocks the CPU until the GPU has finished and the results are available in host memory. If the pointer mode is set to HIPBLAS_POINTER_MODE_DEVICE, this function returns immediately and synchronization is required to read the results.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
a – [inout] device array of device pointers storing each single input vector element a_i, overwritten with r_i.
b – [inout] device array of device pointers storing each single input vector element b_i, overwritten with z_i.
c – [inout] device array of device pointers storing each cosine element of Givens rotation for the batch.
s – [inout] device array of device pointers storing each sine element of Givens rotation for the batch.
batchCount – [in] [int] number of batches (length of arrays a, b, c, and s).

hipblasStatus_t hipblasSrotgStridedBatched(hipblasHandle_t handle, float *a, hipblasStride stridea, float *b, hipblasStride strideb, float *c, hipblasStride stridec, float *s, hipblasStride strides, int batchCount)#

hipblasStatus_t hipblasDrotgStridedBatched(hipblasHandle_t handle, double *a, hipblasStride stridea, double *b, hipblasStride strideb, double *c, hipblasStride stridec, double *s, hipblasStride strides, int batchCount)#

hipblasStatus_t hipblasCrotgStridedBatched(hipblasHandle_t handle, hipblasComplex *a, hipblasStride stridea, hipblasComplex *b, hipblasStride strideb, float *c, hipblasStride stridec, hipblasComplex *s, hipblasStride strides, int batchCount)#

hipblasStatus_t hipblasZrotgStridedBatched(hipblasHandle_t handle, hipblasDoubleComplex *a, hipblasStride stridea, hipblasDoubleComplex *b, hipblasStride strideb, double *c, hipblasStride stridec, hipblasDoubleComplex *s, hipblasStride strides, int batchCount)#

BLAS Level 1 API.

rotgStridedBatched creates the Givens rotation matrix for the strided batched vectors (a_i b_i), for i = 1, …, batchCount. a, b, c, and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode. If the pointer mode is set to HIPBLAS_POINTER_MODE_HOST, this function blocks the CPU until the GPU has finished and the results are available in host memory. If the pointer mode is set to HIPBLAS_POINTER_MODE_HOST, this function returns immediately and synchronization is required to read the results.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
a – [inout] device strided_batched pointer or host strided_batched pointer to first single input vector element a_1, overwritten with r.
stridea – [in] [hipblasStride] distance between elements of a in batch (distance between a_i and a_(i + 1))
b – [inout] device strided_batched pointer or host strided_batched pointer to first single input vector element b_1, overwritten with z.
strideb – [in] [hipblasStride] distance between elements of b in batch (distance between b_i and b_(i + 1))
c – [inout] device strided_batched pointer or host strided_batched pointer to first cosine element of Givens rotations c_1.
stridec – [in] [hipblasStride] distance between elements of c in batch (distance between c_i and c_(i + 1))
s – [inout] device strided_batched pointer or host strided_batched pointer to sine element of Givens rotations s_1.
strides – [in] [hipblasStride] distance between elements of s in batch (distance between s_i and s_(i + 1))
batchCount – [in] [int] number of batches (length of arrays a, b, c, and s).

hipblasXrotm + Batched, StridedBatched #

hipblasStatus_t hipblasSrotm(hipblasHandle_t handle, int n, float *x, int incx, float *y, int incy, const float *param)#

hipblasStatus_t hipblasDrotm(hipblasHandle_t handle, int n, double *x, int incx, double *y, int incy, const double *param)#

BLAS Level 1 API.

rotm applies the modified Givens rotation matrix defined by param to vectors x and y.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : s,d

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in the x and y vectors.
x – [inout] device pointer storing vector x.
incx – [in] [int] specifies the increment between elements of x.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment between elements of y.
param – [in] device vector or host vector of 5 elements defining the rotation. param[0] = flag param[1] = H11 param[2] = H21 param[3] = H12 param[4] = H22 The flag parameter defines the form of H: flag = -1 => H = ( H11 H12 H21 H22 ) flag = 0 => H = ( 1.0 H12 H21 1.0 ) flag = 1 => H = ( H11 1.0 -1.0 H22 ) flag = -2 => H = ( 1.0 0.0 0.0 1.0 ) param may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

hipblasStatus_t hipblasSrotmBatched(hipblasHandle_t handle, int n, float *const x[], int incx, float *const y[], int incy, const float *const param[], int batchCount)#

hipblasStatus_t hipblasDrotmBatched(hipblasHandle_t handle, int n, double *const x[], int incx, double *const y[], int incy, const double *const param[], int batchCount)#

BLAS Level 1 API.

rotmBatched applies the modified Givens rotation matrix defined by param_i to batched vectors x_i and y_i, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in the x and y vectors.
x – [inout] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment between elements of each x_i.
y – [inout] device array of device pointers storing each vector y_1.
incy – [in] [int] specifies the increment between elements of each y_i.
param – [in] device array of device vectors of 5 elements defining the rotation. param[0] = flag param[1] = H11 param[2] = H21 param[3] = H12 param[4] = H22 The flag parameter defines the form of H: flag = -1 => H = ( H11 H12 H21 H22 ) flag = 0 => H = ( 1.0 H12 H21 1.0 ) flag = 1 => H = ( H11 1.0 -1.0 H22 ) flag = -2 => H = ( 1.0 0.0 0.0 1.0 ) param may ONLY be stored on the device for the batched version of this function.
batchCount – [in] [int] the number of x and y arrays, i.e. the number of batches.

hipblasStatus_t hipblasSrotmStridedBatched(hipblasHandle_t handle, int n, float *x, int incx, hipblasStride stridex, float *y, int incy, hipblasStride stridey, const float *param, hipblasStride strideParam, int batchCount)#

hipblasStatus_t hipblasDrotmStridedBatched(hipblasHandle_t handle, int n, double *x, int incx, hipblasStride stridex, double *y, int incy, hipblasStride stridey, const double *param, hipblasStride strideParam, int batchCount)#

BLAS Level 1 API.

rotmStridedBatched applies the modified Givens rotation matrix defined by param_i to strided batched vectors x_i and y_i, for i = 1, …, batchCount

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in the x and y vectors.
x – [inout] device pointer pointing to first strided batched vector x_1.
incx – [in] [int] specifies the increment between elements of each x_i.
stridex – [in] [hipblasStride] specifies the increment between the beginning of x_i and x_(i + 1)
y – [inout] device pointer pointing to first strided batched vector y_1.
incy – [in] [int] specifies the increment between elements of each y_i.
stridey – [in] [hipblasStride] specifies the increment between the beginning of y_i and y_(i + 1)
param – [in] device pointer pointing to first array of 5 elements defining the rotation (param_1). param[0] = flag param[1] = H11 param[2] = H21 param[3] = H12 param[4] = H22 The flag parameter defines the form of H: flag = -1 => H = ( H11 H12 H21 H22 ) flag = 0 => H = ( 1.0 H12 H21 1.0 ) flag = 1 => H = ( H11 1.0 -1.0 H22 ) flag = -2 => H = ( 1.0 0.0 0.0 1.0 ) param may ONLY be stored on the device for the strided_batched version of this function.
strideParam – [in] [hipblasStride] specifies the increment between the beginning of param_i and param_(i + 1)
batchCount – [in] [int] the number of x and y arrays, i.e. the number of batches.

hipblasXrotmg + Batched, StridedBatched #

hipblasStatus_t hipblasSrotmg(hipblasHandle_t handle, float *d1, float *d2, float *x1, const float *y1, float *param)#

hipblasStatus_t hipblasDrotmg(hipblasHandle_t handle, double *d1, double *d2, double *x1, const double *y1, double *param)#

BLAS Level 1 API.

rotmg creates the modified Givens rotation matrix for the vector (d1 * x1, d2 * y1). Parameters may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode. If the pointer mode is set to HIPBLAS_POINTER_MODE_HOST, this function blocks the CPU until the GPU has finished and the results are available in host memory. If the pointer mode is set to HIPBLAS_POINTER_MODE_DEVICE, this function returns immediately and synchronization is required to read the results.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : s,d

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
d1 – [inout] device pointer or host pointer to input scalar that is overwritten.
d2 – [inout] device pointer or host pointer to input scalar that is overwritten.
x1 – [inout] device pointer or host pointer to input scalar that is overwritten.
y1 – [in] device pointer or host pointer to input scalar.
param – [out] device vector or host vector of 5 elements defining the rotation. param[0] = flag param[1] = H11 param[2] = H21 param[3] = H12 param[4] = H22 The flag parameter defines the form of H: flag = -1 => H = ( H11 H12 H21 H22 ) flag = 0 => H = ( 1.0 H12 H21 1.0 ) flag = 1 => H = ( H11 1.0 -1.0 H22 ) flag = -2 => H = ( 1.0 0.0 0.0 1.0 ) param may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

hipblasStatus_t hipblasSrotmgBatched(hipblasHandle_t handle, float *const d1[], float *const d2[], float *const x1[], const float *const y1[], float *const param[], int batchCount)#

hipblasStatus_t hipblasDrotmgBatched(hipblasHandle_t handle, double *const d1[], double *const d2[], double *const x1[], const double *const y1[], double *const param[], int batchCount)#

BLAS Level 1 API.

rotmgBatched creates the modified Givens rotation matrix for the batched vectors (d1_i * x1_i, d2_i * y1_i), for i = 1, …, batchCount. Parameters may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode. If the pointer mode is set to HIPBLAS_POINTER_MODE_HOST, this function blocks the CPU until the GPU has finished and the results are available in host memory. If the pointer mode is set to HIPBLAS_POINTER_MODE_DEVICE, this function returns immediately and synchronization is required to read the results.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
d1 – [inout] device batched array or host batched array of input scalars that is overwritten.
d2 – [inout] device batched array or host batched array of input scalars that is overwritten.
x1 – [inout] device batched array or host batched array of input scalars that is overwritten.
y1 – [in] device batched array or host batched array of input scalars.
param – [out] device batched array or host batched array of vectors of 5 elements defining the rotation. param[0] = flag param[1] = H11 param[2] = H21 param[3] = H12 param[4] = H22 The flag parameter defines the form of H: flag = -1 => H = ( H11 H12 H21 H22 ) flag = 0 => H = ( 1.0 H12 H21 1.0 ) flag = 1 => H = ( H11 1.0 -1.0 H22 ) flag = -2 => H = ( 1.0 0.0 0.0 1.0 ) param may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.
batchCount – [in] [int] the number of instances in the batch.

hipblasStatus_t hipblasSrotmgStridedBatched(hipblasHandle_t handle, float *d1, hipblasStride strided1, float *d2, hipblasStride strided2, float *x1, hipblasStride stridex1, const float *y1, hipblasStride stridey1, float *param, hipblasStride strideParam, int batchCount)#

hipblasStatus_t hipblasDrotmgStridedBatched(hipblasHandle_t handle, double *d1, hipblasStride strided1, double *d2, hipblasStride strided2, double *x1, hipblasStride stridex1, const double *y1, hipblasStride stridey1, double *param, hipblasStride strideParam, int batchCount)#

BLAS Level 1 API.

rotmgStridedBatched creates the modified Givens rotation matrix for the strided batched vectors (d1_i * x1_i, d2_i * y1_i), for i = 1, …, batchCount. Parameters may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode. If the pointer mode is set to HIPBLAS_POINTER_MODE_HOST, this function blocks the CPU until the GPU has finished and the results are available in host memory. If the pointer mode is set to HIPBLAS_POINTER_MODE_DEVICE, this function returns immediately and synchronization is required to read the results.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
d1 – [inout] device strided_batched array or host strided_batched array of input scalars that is overwritten.
strided1 – [in] [hipblasStride] specifies the increment between the beginning of d1_i and d1_(i+1)
d2 – [inout] device strided_batched array or host strided_batched array of input scalars that is overwritten.
strided2 – [in] [hipblasStride] specifies the increment between the beginning of d2_i and d2_(i+1)
x1 – [inout] device strided_batched array or host strided_batched array of input scalars that is overwritten.
stridex1 – [in] [hipblasStride] specifies the increment between the beginning of x1_i and x1_(i+1)
y1 – [in] device strided_batched array or host strided_batched array of input scalars.
stridey1 – [in] [hipblasStride] specifies the increment between the beginning of y1_i and y1_(i+1)
param – [out] device stridedBatched array or host stridedBatched array of vectors of 5 elements defining the rotation. param[0] = flag param[1] = H11 param[2] = H21 param[3] = H12 param[4] = H22 The flag parameter defines the form of H: flag = -1 => H = ( H11 H12 H21 H22 ) flag = 0 => H = ( 1.0 H12 H21 1.0 ) flag = 1 => H = ( H11 1.0 -1.0 H22 ) flag = -2 => H = ( 1.0 0.0 0.0 1.0 ) param may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.
strideParam – [in] [hipblasStride] specifies the increment between the beginning of param_i and param_(i + 1)
batchCount – [in] [int] the number of instances in the batch.

hipblasXscal + Batched, StridedBatched #

hipblasStatus_t hipblasSscal(hipblasHandle_t handle, int n, const float *alpha, float *x, int incx)#

hipblasStatus_t hipblasDscal(hipblasHandle_t handle, int n, const double *alpha, double *x, int incx)#

hipblasStatus_t hipblasCscal(hipblasHandle_t handle, int n, const hipblasComplex *alpha, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasCsscal(hipblasHandle_t handle, int n, const float *alpha, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasZscal(hipblasHandle_t handle, int n, const hipblasDoubleComplex *alpha, hipblasDoubleComplex *x, int incx)#

hipblasStatus_t hipblasZdscal(hipblasHandle_t handle, int n, const double *alpha, hipblasDoubleComplex *x, int incx)#

BLAS Level 1 API.

scal scales each element of vector x with scalar alpha.

x := alpha * x

Supported precisions in rocBLAS : s,d,c,z,cs,zd
Supported precisions in cuBLAS : s,d,c,z,cs,zd

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
alpha – [in] device pointer or host pointer for the scalar alpha.
x – [inout] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.

hipblasStatus_t hipblasSscalBatched(hipblasHandle_t handle, int n, const float *alpha, float *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasDscalBatched(hipblasHandle_t handle, int n, const double *alpha, double *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCscalBatched(hipblasHandle_t handle, int n, const hipblasComplex *alpha, hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZscalBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *alpha, hipblasDoubleComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCsscalBatched(hipblasHandle_t handle, int n, const float *alpha, hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZdscalBatched(hipblasHandle_t handle, int n, const double *alpha, hipblasDoubleComplex *const x[], int incx, int batchCount)#

BLAS Level 1 API.

scalBatched scales each element of vector x_i with scalar alpha, for i = 1, … , batchCount.

 x_i := alpha * x_i

where (x_i) is the i-th instance of the batch.

Supported precisions in rocBLAS : s,d,c,z,cs,zd
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i.
alpha – [in] host pointer or device pointer for the scalar alpha.
x – [inout] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
batchCount – [in] [int] specifies the number of batches in x.

hipblasStatus_t hipblasSscalStridedBatched(hipblasHandle_t handle, int n, const float *alpha, float *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasDscalStridedBatched(hipblasHandle_t handle, int n, const double *alpha, double *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCscalStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *alpha, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZscalStridedBatched(hipblasHandle_t handle, int n, const hipblasDoubleComplex *alpha, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCsscalStridedBatched(hipblasHandle_t handle, int n, const float *alpha, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZdscalStridedBatched(hipblasHandle_t handle, int n, const double *alpha, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

BLAS Level 1 API.

scalStridedBatched scales each element of vector x_i with scalar alpha, for i = 1, … , batchCount.

 x_i := alpha * x_i ,

where (x_i) is the i-th instance of the batch.

Supported precisions in rocBLAS : s,d,c,z,cs,zd
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i.
alpha – [in] host pointer or device pointer for the scalar alpha.
x – [inout] device pointer to the first vector (x_1) in the batch.
incx – [in] [int] specifies the increment for the elements of x.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stride_x, however the user should take care to ensure that stride_x is of appropriate size, for a typical case this means stride_x >= n * incx.
batchCount – [in] [int] specifies the number of batches in x.

hipblasXswap + Batched, StridedBatched #

hipblasStatus_t hipblasSswap(hipblasHandle_t handle, int n, float *x, int incx, float *y, int incy)#

hipblasStatus_t hipblasDswap(hipblasHandle_t handle, int n, double *x, int incx, double *y, int incy)#

hipblasStatus_t hipblasCswap(hipblasHandle_t handle, int n, hipblasComplex *x, int incx, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZswap(hipblasHandle_t handle, int n, hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *y, int incy)#

BLAS Level 1 API.

swap interchanges vectors x and y.

y := x; x := y

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
x – [inout] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasSswapBatched(hipblasHandle_t handle, int n, float *x[], int incx, float *y[], int incy, int batchCount)#

hipblasStatus_t hipblasDswapBatched(hipblasHandle_t handle, int n, double *x[], int incx, double *y[], int incy, int batchCount)#

hipblasStatus_t hipblasCswapBatched(hipblasHandle_t handle, int n, hipblasComplex *x[], int incx, hipblasComplex *y[], int incy, int batchCount)#

hipblasStatus_t hipblasZswapBatched(hipblasHandle_t handle, int n, hipblasDoubleComplex *x[], int incx, hipblasDoubleComplex *y[], int incy, int batchCount)#

BLAS Level 1 API.

swapBatched interchanges vectors x_i and y_i, for i = 1 , … , batchCount

y_i := x_i; x_i := y_i

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [inout] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSswapStridedBatched(hipblasHandle_t handle, int n, float *x, int incx, hipblasStride stridex, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDswapStridedBatched(hipblasHandle_t handle, int n, double *x, int incx, hipblasStride stridex, double *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasCswapStridedBatched(hipblasHandle_t handle, int n, hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZswapStridedBatched(hipblasHandle_t handle, int n, hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 1 API.

swapStridedBatched interchanges vectors x_i and y_i, for i = 1 , … , batchCount

y_i := x_i; x_i := y_i

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [inout] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of x.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stride_x, however the user should take care to ensure that stride_x is of appropriate size, for a typical case this means stride_x >= n * incx.
y – [inout] device pointer to the first vector y_1.
incy – [in] [int] specifies the increment for the elements of y.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1). There are no restrictions placed on stride_x, however the user should take care to ensure that stride_y is of appropriate size, for a typical case this means stride_y >= n * incy. stridey should be non zero.
batchCount – [in] [int] number of instances in the batch.

Level 2 BLAS#

hipblasXgbmv + Batched, StridedBatched #

hipblasStatus_t hipblasSgbmv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const float *alpha, const float *AP, int lda, const float *x, int incx, const float *beta, float *y, int incy)#

hipblasStatus_t hipblasDgbmv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const double *alpha, const double *AP, int lda, const double *x, int incx, const double *beta, double *y, int incy)#

hipblasStatus_t hipblasCgbmv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *x, int incx, const hipblasComplex *beta, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZgbmv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy)#

BLAS Level 2 API.

gbmv performs one of the matrix-vector operations

y := alpha*A*x    + beta*y,   or
y := alpha*A**T*x + beta*y,   or
y := alpha*A**H*x + beta*y,

where alpha and beta are scalars, x and y are vectors and A is an m by n banded matrix with kl sub-diagonals and ku super-diagonals.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
trans – [in] [hipblasOperation_t] indicates whether matrix A is tranposed (conjugated) or not
m – [in] [int] number of rows of matrix A
n – [in] [int] number of columns of matrix A
kl – [in] [int] number of sub-diagonals of A
ku – [in] [int] number of super-diagonals of A
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer storing banded matrix A. Leading (kl + ku + 1) by n part of the matrix contains the coefficients of the banded matrix. The leading diagonal resides in row (ku + 1) with the first super-diagonal above on the RHS of row ku. The first sub-diagonal resides below on the LHS of row ku + 2. This propogates up and down across sub/super-diagonals. Ex: (m = n = 7; ku = 2, kl = 2) 1 2 3 0 0 0 0 0 0 3 3 3 3 3 4 1 2 3 0 0 0 0 2 2 2 2 2 2 5 4 1 2 3 0 0 -—> 1 1 1 1 1 1 1 0 5 4 1 2 3 0 4 4 4 4 4 4 0 0 0 5 4 1 2 0 5 5 5 5 5 0 0 0 0 0 5 4 1 2 0 0 0 0 0 0 0 0 0 0 0 5 4 1 0 0 0 0 0 0 0 Note that the empty elements which don’t correspond to data will not be referenced.
lda – [in] [int] specifies the leading dimension of A. Must be >= (kl + ku + 1)
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasSgbmvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const float *alpha, const float *const AP[], int lda, const float *const x[], int incx, const float *beta, float *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasDgbmvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const double *alpha, const double *const AP[], int lda, const double *const x[], int incx, const double *beta, double *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasCgbmvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const x[], int incx, const hipblasComplex *beta, hipblasComplex *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasZgbmvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const y[], int incy, int batchCount)#

BLAS Level 2 API.

gbmvBatched performs one of the matrix-vector operations

y_i := alpha*A_i*x_i    + beta*y_i,   or
y_i := alpha*A_i**T*x_i + beta*y_i,   or
y_i := alpha*A_i**H*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an m by n banded matrix with kl sub-diagonals and ku super-diagonals, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
trans – [in] [hipblasOperation_t] indicates whether matrix A is tranposed (conjugated) or not
m – [in] [int] number of rows of each matrix A_i
n – [in] [int] number of columns of each matrix A_i
kl – [in] [int] number of sub-diagonals of each A_i
ku – [in] [int] number of super-diagonals of each A_i
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device array of device pointers storing each banded matrix A_i. Leading (kl + ku + 1) by n part of the matrix contains the coefficients of the banded matrix. The leading diagonal resides in row (ku + 1) with the first super-diagonal above on the RHS of row ku. The first sub-diagonal resides below on the LHS of row ku + 2. This propogates up and down across sub/super-diagonals. Ex: (m = n = 7; ku = 2, kl = 2) 1 2 3 0 0 0 0 0 0 3 3 3 3 3 4 1 2 3 0 0 0 0 2 2 2 2 2 2 5 4 1 2 3 0 0 -—> 1 1 1 1 1 1 1 0 5 4 1 2 3 0 4 4 4 4 4 4 0 0 0 5 4 1 2 0 5 5 5 5 5 0 0 0 0 0 5 4 1 2 0 0 0 0 0 0 0 0 0 0 0 5 4 1 0 0 0 0 0 0 0 Note that the empty elements which don’t correspond to data will not be referenced.
lda – [in] [int] specifies the leading dimension of each A_i. Must be >= (kl + ku + 1)
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
batchCount – [in] [int] specifies the number of instances in the batch.

hipblasStatus_t hipblasSgbmvStridedBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *x, int incx, hipblasStride stridex, const float *beta, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDgbmvStridedBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *x, int incx, hipblasStride stridex, const double *beta, double *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasCgbmvStridedBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *beta, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZgbmvStridedBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, int kl, int ku, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

gbmvStridedBatched performs one of the matrix-vector operations

y_i := alpha*A_i*x_i    + beta*y_i,   or
y_i := alpha*A_i**T*x_i + beta*y_i,   or
y_i := alpha*A_i**H*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an m by n banded matrix with kl sub-diagonals and ku super-diagonals, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
trans – [in] [hipblasOperation_t] indicates whether matrix A is tranposed (conjugated) or not
m – [in] [int] number of rows of matrix A
n – [in] [int] number of columns of matrix A
kl – [in] [int] number of sub-diagonals of A
ku – [in] [int] number of super-diagonals of A
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer to first banded matrix (A_1). Leading (kl + ku + 1) by n part of the matrix contains the coefficients of the banded matrix. The leading diagonal resides in row (ku + 1) with the first super-diagonal above on the RHS of row ku. The first sub-diagonal resides below on the LHS of row ku + 2. This propogates up and down across sub/super-diagonals. Ex: (m = n = 7; ku = 2, kl = 2) 1 2 3 0 0 0 0 0 0 3 3 3 3 3 4 1 2 3 0 0 0 0 2 2 2 2 2 2 5 4 1 2 3 0 0 -—> 1 1 1 1 1 1 1 0 5 4 1 2 3 0 4 4 4 4 4 4 0 0 0 5 4 1 2 0 5 5 5 5 5 0 0 0 0 0 5 4 1 2 0 0 0 0 0 0 0 0 0 0 0 5 4 1 0 0 0 0 0 0 0 Note that the empty elements which don’t correspond to data will not be referenced.
lda – [in] [int] specifies the leading dimension of A. Must be >= (kl + ku + 1)
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
x – [in] device pointer to first vector (x_1).
incx – [in] [int] specifies the increment for the elements of x.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1)
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device pointer to first vector (y_1).
incy – [in] [int] specifies the increment for the elements of y.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (x_i+1)
batchCount – [in] [int] specifies the number of instances in the batch.

hipblasXgemv + Batched, StridedBatched #

hipblasStatus_t hipblasSgemv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const float *alpha, const float *AP, int lda, const float *x, int incx, const float *beta, float *y, int incy)#

hipblasStatus_t hipblasDgemv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const double *alpha, const double *AP, int lda, const double *x, int incx, const double *beta, double *y, int incy)#

hipblasStatus_t hipblasCgemv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *x, int incx, const hipblasComplex *beta, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZgemv(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy)#

BLAS Level 2 API.

gemv performs one of the matrix-vector operations

y := alpha*A*x    + beta*y,   or
y := alpha*A**T*x + beta*y,   or
y := alpha*A**H*x + beta*y,

where alpha and beta are scalars, x and y are vectors and A is an m by n matrix.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
trans – [in] [hipblasOperation_t] indicates whether matrix A is tranposed (conjugated) or not
m – [in] [int] number of rows of matrix A
n – [in] [int] number of columns of matrix A
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasSgemvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const float *alpha, const float *const AP[], int lda, const float *const x[], int incx, const float *beta, float *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasDgemvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const double *alpha, const double *const AP[], int lda, const double *const x[], int incx, const double *beta, double *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasCgemvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const x[], int incx, const hipblasComplex *beta, hipblasComplex *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasZgemvBatched(hipblasHandle_t handle, hipblasOperation_t trans, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const y[], int incy, int batchCount)#

BLAS Level 2 API.

gemvBatched performs a batch of matrix-vector operations

y_i := alpha*A_i*x_i    + beta*y_i,   or
y_i := alpha*A_i**T*x_i + beta*y_i,   or
y_i := alpha*A_i**H*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an m by n matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
trans – [in] [hipblasOperation_t] indicates whether matrices A_i are tranposed (conjugated) or not
m – [in] [int] number of rows of each matrix A_i
n – [in] [int] number of columns of each matrix A_i
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device array of device pointers storing each matrix A_i.
lda – [in] [int] specifies the leading dimension of each matrix A_i.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each vector x_i.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each vector y_i.
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasSgemvStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, int m, int n, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *x, int incx, hipblasStride stridex, const float *beta, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDgemvStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, int m, int n, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *x, int incx, hipblasStride stridex, const double *beta, double *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasCgemvStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *beta, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZgemvStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

gemvStridedBatched performs a batch of matrix-vector operations

y_i := alpha*A_i*x_i    + beta*y_i,   or
y_i := alpha*A_i**T*x_i + beta*y_i,   or
y_i := alpha*A_i**H*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an m by n matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] indicates whether matrices A_i are tranposed (conjugated) or not
m – [in] [int] number of rows of matrices A_i
n – [in] [int] number of columns of matrices A_i
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer to the first matrix (A_1) in the batch.
lda – [in] [int] specifies the leading dimension of matrices A_i.
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
x – [in] device pointer to the first vector (x_1) in the batch.
incx – [in] [int] specifies the increment for the elements of vectors x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stridex, however the user should take care to ensure that stridex is of appropriate size. When trans equals HIPBLAS_OP_N this typically means stridex >= n * incx, otherwise stridex >= m * incx.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device pointer to the first vector (y_1) in the batch.
incy – [in] [int] specifies the increment for the elements of vectors y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1). There are no restrictions placed on stridey, however the user should take care to ensure that stridey is of appropriate size. When trans equals HIPBLAS_OP_N this typically means stridey >= m * incy, otherwise stridey >= n * incy. stridey should be non zero.
batchCount – [in] [int] number of instances in the batch

hipblasXger + Batched, StridedBatched #

hipblasStatus_t hipblasSger(hipblasHandle_t handle, int m, int n, const float *alpha, const float *x, int incx, const float *y, int incy, float *AP, int lda)#

hipblasStatus_t hipblasDger(hipblasHandle_t handle, int m, int n, const double *alpha, const double *x, int incx, const double *y, int incy, double *AP, int lda)#

hipblasStatus_t hipblasCgeru(hipblasHandle_t handle, int m, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, const hipblasComplex *y, int incy, hipblasComplex *AP, int lda)#

hipblasStatus_t hipblasCgerc(hipblasHandle_t handle, int m, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, const hipblasComplex *y, int incy, hipblasComplex *AP, int lda)#

hipblasStatus_t hipblasZgeru(hipblasHandle_t handle, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *y, int incy, hipblasDoubleComplex *AP, int lda)#

hipblasStatus_t hipblasZgerc(hipblasHandle_t handle, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *y, int incy, hipblasDoubleComplex *AP, int lda)#

BLAS Level 2 API.

ger,geru,gerc performs the matrix-vector operations

A := A + alpha*x*y**T , OR
A := A + alpha*x*y**H for gerc

where alpha is a scalar, x and y are vectors, and A is an m by n matrix.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
m – [in] [int] the number of rows of the matrix A.
n – [in] [int] the number of columns of the matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [in] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.
AP – [inout] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.

hipblasStatus_t hipblasSgerBatched(hipblasHandle_t handle, int m, int n, const float *alpha, const float *const x[], int incx, const float *const y[], int incy, float *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasDgerBatched(hipblasHandle_t handle, int m, int n, const double *alpha, const double *const x[], int incx, const double *const y[], int incy, double *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasCgeruBatched(hipblasHandle_t handle, int m, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, const hipblasComplex *const y[], int incy, hipblasComplex *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasCgercBatched(hipblasHandle_t handle, int m, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, const hipblasComplex *const y[], int incy, hipblasComplex *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasZgeruBatched(hipblasHandle_t handle, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *const y[], int incy, hipblasDoubleComplex *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasZgercBatched(hipblasHandle_t handle, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *const y[], int incy, hipblasDoubleComplex *const AP[], int lda, int batchCount)#

BLAS Level 2 API.

gerBatched,geruBatched,gercBatched performs a batch of the matrix-vector operations

A := A + alpha*x*y**T , OR
A := A + alpha*x*y**H for gerc

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha is a scalar, x_i and y_i are vectors and A_i is an m by n matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
m – [in] [int] the number of rows of each matrix A_i.
n – [in] [int] the number of columns of eaceh matrix A_i.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each vector x_i.
y – [in] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each vector y_i.
AP – [inout] device array of device pointers storing each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i.
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasSgerStridedBatched(hipblasHandle_t handle, int m, int n, const float *alpha, const float *x, int incx, hipblasStride stridex, const float *y, int incy, hipblasStride stridey, float *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasDgerStridedBatched(hipblasHandle_t handle, int m, int n, const double *alpha, const double *x, int incx, hipblasStride stridex, const double *y, int incy, hipblasStride stridey, double *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasCgeruStridedBatched(hipblasHandle_t handle, int m, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *y, int incy, hipblasStride stridey, hipblasComplex *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasCgercStridedBatched(hipblasHandle_t handle, int m, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *y, int incy, hipblasStride stridey, hipblasComplex *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZgeruStridedBatched(hipblasHandle_t handle, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *y, int incy, hipblasStride stridey, hipblasDoubleComplex *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZgercStridedBatched(hipblasHandle_t handle, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *y, int incy, hipblasStride stridey, hipblasDoubleComplex *AP, int lda, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

gerStridedBatched,geruStridedBatched,gercStridedBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*y_i**T, OR
A_i := A_i + alpha*x_i*y_i**H  for gerc

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha is a scalar, x_i and y_i are vectors and A_i is an m by n matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
m – [in] [int] the number of rows of each matrix A_i.
n – [in] [int] the number of columns of each matrix A_i.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer to the first vector (x_1) in the batch.
incx – [in] [int] specifies the increments for the elements of each vector x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stridex, however the user should take care to ensure that stridex is of appropriate size, for a typical case this means stridex >= m * incx.
y – [inout] device pointer to the first vector (y_1) in the batch.
incy – [in] [int] specifies the increment for the elements of each vector y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1). There are no restrictions placed on stridey, however the user should take care to ensure that stridey is of appropriate size, for a typical case this means stridey >= n * incy.
AP – [inout] device pointer to the first matrix (A_1) in the batch.
lda – [in] [int] specifies the leading dimension of each A_i.
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
batchCount – [in] [int] number of instances in the batch

hipblasXhbmv + Batched, StridedBatched #

hipblasStatus_t hipblasChbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *x, int incx, const hipblasComplex *beta, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZhbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy)#

BLAS Level 2 API.

hbmv performs the matrix-vector operations

y := alpha*A*x + beta*y

where alpha and beta are scalars, x and y are n element vectors and A is an n by n Hermitian band matrix, with k super-diagonals.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

if uplo == HIPBLAS_FILL_MODE_LOWER: The leading (k + 1) by n part of A must contain the lower triangular band part of the Hermitian matrix, with the leading diagonal in row (1), the first sub-diagonal on the LHS of row 2, etc. The bottom right k by k triangle of A will not be referenced. Ex (lower, lda = 2, n = 4, k = 1): A Represented matrix (1,0) (2,0) (3,0) (4,0) (1, 0) (5,-9) (0, 0) (0, 0) (5,9) (6,8) (7,7) (0,0) (5, 9) (2, 0) (6,-8) (0, 0) (0, 0) (6, 8) (3, 0) (7,-7) (0, 0) (0, 0) (7, 7) (4, 0)

As a Hermitian matrix, the imaginary part of the main diagonal of A will not be referenced and is assumed to be == 0.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A is being supplied. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A is being supplied.
n – [in] [int] the order of the matrix A.
k – [in] [int] the number of super-diagonals of the matrix A. Must be >= 0.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer storing matrix A. Of dimension (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The leading (k + 1) by n part of A must contain the upper triangular band part of the Hermitian matrix, with the leading diagonal in row (k + 1), the first super-diagonal on the RHS of row k, etc. The top left k by x triangle of A will not be referenced. Ex (upper, lda = n = 4, k = 1): A Represented matrix (0,0) (5,9) (6,8) (7,7) (1, 0) (5, 9) (0, 0) (0, 0) (1,0) (2,0) (3,0) (4,0) (5,-9) (2, 0) (6, 8) (0, 0) (0,0) (0,0) (0,0) (0,0) (0, 0) (6,-8) (3, 0) (7, 7) (0,0) (0,0) (0,0) (0,0) (0, 0) (0, 0) (7,-7) (4, 0)
lda – [in] [int] specifies the leading dimension of A. must be >= k + 1
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasChbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const x[], int incx, const hipblasComplex *beta, hipblasComplex *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasZhbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const y[], int incy, int batchCount)#

BLAS Level 2 API.

hbmvBatched performs one of the matrix-vector operations

y_i := alpha*A_i*x_i + beta*y_i

where alpha and beta are scalars, x_i and y_i are n element vectors and A_i is an n by n Hermitian band matrix with k super-diagonals, for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

if uplo == HIPBLAS_FILL_MODE_LOWER: The leading (k + 1) by n part of each A_i must contain the lower triangular band part of the Hermitian matrix, with the leading diagonal in row (1), the first sub-diagonal on the LHS of row 2, etc. The bottom right k by k triangle of each A_i will not be referenced. Ex (lower, lda = 2, n = 4, k = 1): A Represented matrix (1,0) (2,0) (3,0) (4,0) (1, 0) (5,-9) (0, 0) (0, 0) (5,9) (6,8) (7,7) (0,0) (5, 9) (2, 0) (6,-8) (0, 0) (0, 0) (6, 8) (3, 0) (7,-7) (0, 0) (0, 0) (7, 7) (4, 0)

As a Hermitian matrix, the imaginary part of the main diagonal of each A_i will not be referenced and is assumed to be == 0.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is being supplied. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is being supplied.
n – [in] [int] the order of each matrix A_i.
k – [in] [int] the number of super-diagonals of each matrix A_i. Must be >= 0.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device array of device pointers storing each matrix_i A of dimension (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The leading (k + 1) by n part of each A_i must contain the upper triangular band part of the Hermitian matrix, with the leading diagonal in row (k + 1), the first super-diagonal on the RHS of row k, etc. The top left k by x triangle of each A_i will not be referenced. Ex (upper, lda = n = 4, k = 1): A Represented matrix (0,0) (5,9) (6,8) (7,7) (1, 0) (5, 9) (0, 0) (0, 0) (1,0) (2,0) (3,0) (4,0) (5,-9) (2, 0) (6, 8) (0, 0) (0,0) (0,0) (0,0) (0,0) (0, 0) (6,-8) (3, 0) (7, 7) (0,0) (0,0) (0,0) (0,0) (0, 0) (0, 0) (7,-7) (4, 0)
lda – [in] [int] specifies the leading dimension of each A_i. must be >= max(1, n)
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of y.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasChbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *beta, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZhbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

hbmvStridedBatched performs one of the matrix-vector operations

y_i := alpha*A_i*x_i + beta*y_i

where alpha and beta are scalars, x_i and y_i are n element vectors and A_i is an n by n Hermitian band matrix with k super-diagonals, for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

if uplo == HIPBLAS_FILL_MODE_LOWER: The leading (k + 1) by n part of each A_i must contain the lower triangular band part of the Hermitian matrix, with the leading diagonal in row (1), the first sub-diagonal on the LHS of row 2, etc. The bottom right k by k triangle of each A_i will not be referenced. Ex (lower, lda = 2, n = 4, k = 1): A Represented matrix (1,0) (2,0) (3,0) (4,0) (1, 0) (5,-9) (0, 0) (0, 0) (5,9) (6,8) (7,7) (0,0) (5, 9) (2, 0) (6,-8) (0, 0) (0, 0) (6, 8) (3, 0) (7,-7) (0, 0) (0, 0) (7, 7) (4, 0)

As a Hermitian matrix, the imaginary part of the main diagonal of each A_i will not be referenced and is assumed to be == 0.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is being supplied. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is being supplied.
n – [in] [int] the order of each matrix A_i.
k – [in] [int] the number of super-diagonals of each matrix A_i. Must be >= 0.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device array pointing to the first matrix A_1. Each A_i is of dimension (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The leading (k + 1) by n part of each A_i must contain the upper triangular band part of the Hermitian matrix, with the leading diagonal in row (k + 1), the first super-diagonal on the RHS of row k, etc. The top left k by x triangle of each A_i will not be referenced. Ex (upper, lda = n = 4, k = 1): A Represented matrix (0,0) (5,9) (6,8) (7,7) (1, 0) (5, 9) (0, 0) (0, 0) (1,0) (2,0) (3,0) (4,0) (5,-9) (2, 0) (6, 8) (0, 0) (0,0) (0,0) (0,0) (0,0) (0, 0) (6,-8) (3, 0) (7, 7) (0,0) (0,0) (0,0) (0,0) (0, 0) (0, 0) (7,-7) (4, 0)
lda – [in] [int] specifies the leading dimension of each A_i. must be >= max(1, n)
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
x – [in] device array pointing to the first vector y_1.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1)
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array pointing to the first vector y_1.
incy – [in] [int] specifies the increment for the elements of y.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXhemv + Batched, StridedBatched #

hipblasStatus_t hipblasChemv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *x, int incx, const hipblasComplex *beta, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZhemv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy)#

BLAS Level 2 API.

hemv performs one of the matrix-vector operations

y := alpha*A*x + beta*y

where alpha and beta are scalars, x and y are n element vectors and A is an n by n Hermitian matrix.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: the upper triangular part of the Hermitian matrix A is supplied. HIPBLAS_FILL_MODE_LOWER: the lower triangular part of the Hermitian matrix A is supplied.
n – [in] [int] the order of the matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer storing matrix A. Of dimension (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A must contain the upper triangular part of a Hermitian matrix. The lower triangular part of A will not be referenced. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A must contain the lower triangular part of a Hermitian matrix. The upper triangular part of A will not be referenced. As a Hermitian matrix, the imaginary part of the main diagonal of A will not be referenced and is assumed to be == 0.
lda – [in] [int] specifies the leading dimension of A. must be >= max(1, n)
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasChemvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const x[], int incx, const hipblasComplex *beta, hipblasComplex *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasZhemvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const y[], int incy, int batchCount)#

BLAS Level 2 API.

hemvBatched performs one of the matrix-vector operations

y_i := alpha*A_i*x_i + beta*y_i

where alpha and beta are scalars, x_i and y_i are n element vectors and A_i is an n by n Hermitian matrix, for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: the upper triangular part of the Hermitian matrix A is supplied. HIPBLAS_FILL_MODE_LOWER: the lower triangular part of the Hermitian matrix A is supplied.
n – [in] [int] the order of each matrix A_i.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device array of device pointers storing each matrix A_i of dimension (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i must contain the upper triangular part of a Hermitian matrix. The lower triangular part of each A_i will not be referenced. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i must contain the lower triangular part of a Hermitian matrix. The upper triangular part of each A_i will not be referenced. As a Hermitian matrix, the imaginary part of the main diagonal of each A_i will not be referenced and is assumed to be == 0.
lda – [in] [int] specifies the leading dimension of each A_i. must be >= max(1, n)
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of y.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasChemvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *beta, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZhemvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

hemvStridedBatched performs one of the matrix-vector operations

y_i := alpha*A_i*x_i + beta*y_i

where alpha and beta are scalars, x_i and y_i are n element vectors and A_i is an n by n Hermitian matrix, for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: the upper triangular part of the Hermitian matrix A is supplied. HIPBLAS_FILL_MODE_LOWER: the lower triangular part of the Hermitian matrix A is supplied.
n – [in] [int] the order of each matrix A_i.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device array of device pointers storing each matrix A_i of dimension (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i must contain the upper triangular part of a Hermitian matrix. The lower triangular part of each A_i will not be referenced. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i must contain the lower triangular part of a Hermitian matrix. The upper triangular part of each A_i will not be referenced. As a Hermitian matrix, the imaginary part of the main diagonal of each A_i will not be referenced and is assumed to be == 0.
lda – [in] [int] specifies the leading dimension of each A_i. must be >= max(1, n)
strideA – [in] [hipblasStride] stride from the start of one (A_i) to the next (A_i+1)
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1).
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of y.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1).
batchCount – [in] [int] number of instances in the batch.

hipblasXher + Batched, StridedBatched #

hipblasStatus_t hipblasCher(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const hipblasComplex *x, int incx, hipblasComplex *AP, int lda)#

hipblasStatus_t hipblasZher(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *AP, int lda)#

BLAS Level 2 API.

her performs the matrix-vector operations

A := A + alpha*x*x**H

where alpha is a real scalar, x is a vector, and A is an n by n Hermitian matrix.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A is supplied in A. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A is supplied in A.
n – [in] [int] the number of rows and columns of matrix A, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
AP – [inout] device pointer storing the specified triangular portion of the Hermitian matrix A. Of size (lda * n). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of the Hermitian matrix A is supplied. The lower triangluar portion will not be touched. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of the Hermitian matrix A is supplied. The upper triangular portion will not be touched. Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
lda – [in] [int] specifies the leading dimension of A. Must be at least max(1, n).

hipblasStatus_t hipblasCherBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const hipblasComplex *const x[], int incx, hipblasComplex *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasZherBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const AP[], int lda, int batchCount)#

BLAS Level 2 API.

herBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*x_i**H

where alpha is a real scalar, x_i is a vector, and A_i is an n by n symmetric matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in A. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in A.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
AP – [inout] device array of device pointers storing the specified triangular portion of each Hermitian matrix A_i of at least size ((n * (n + 1)) / 2). Array is of at least size batchCount. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The lower triangular portion of each A_i will not be touched. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The upper triangular portion of each A_i will not be touched. Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
lda – [in] [int] specifies the leading dimension of each A_i. Must be at least max(1, n).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasCherStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZherStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *AP, int lda, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

herStridedBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*x_i**H

where alpha is a real scalar, x_i is a vector, and A_i is an n by n Hermitian matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in A. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in A.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer pointing to the first vector (x_1).
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1).
AP – [inout] device array of device pointers storing the specified triangular portion of each Hermitian matrix A_i. Points to the first matrix (A_1). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The lower triangular portion of each A_i will not be touched. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The upper triangular portion of each A_i will not be touched. Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
lda – [in] [int] specifies the leading dimension of each A_i.
strideA – [in] [hipblasStride] stride from the start of one (A_i) and the next (A_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXher2 + Batched, StridedBatched #

hipblasStatus_t hipblasCher2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, const hipblasComplex *y, int incy, hipblasComplex *AP, int lda)#

hipblasStatus_t hipblasZher2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *y, int incy, hipblasDoubleComplex *AP, int lda)#

BLAS Level 2 API.

her2 performs the matrix-vector operations

A := A + alpha*x*y**H + conj(alpha)*y*x**H

where alpha is a complex scalar, x and y are vectors, and A is an n by n Hermitian matrix.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A is supplied. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A is supplied.
n – [in] [int] the number of rows and columns of matrix A, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [in] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.
AP – [inout] device pointer storing the specified triangular portion of the Hermitian matrix A. Of size (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of the Hermitian matrix A is supplied. The lower triangular portion of A will not be touched. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of the Hermitian matrix A is supplied. The upper triangular portion of A will not be touched. Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
lda – [in] [int] specifies the leading dimension of A. Must be at least max(lda, 1).

hipblasStatus_t hipblasCher2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, const hipblasComplex *const y[], int incy, hipblasComplex *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasZher2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *const y[], int incy, hipblasDoubleComplex *const AP[], int lda, int batchCount)#

BLAS Level 2 API.

her2Batched performs the matrix-vector operations

A_i := A_i + alpha*x_i*y_i**H + conj(alpha)*y_i*x_i**H

where alpha is a complex scalar, x_i and y_i are vectors, and A_i is an n by n Hermitian matrix for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of x.
y – [in] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
AP – [inout] device array of device pointers storing the specified triangular portion of each Hermitian matrix A_i of size (lda, n). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The lower triangular portion of each A_i will not be touched. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The upper triangular portion of each A_i will not be touched. Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
lda – [in] [int] specifies the leading dimension of each A_i. Must be at least max(lda, 1).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasCher2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *y, int incy, hipblasStride stridey, hipblasComplex *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZher2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *y, int incy, hipblasStride stridey, hipblasDoubleComplex *AP, int lda, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

her2StridedBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*y_i**H + conj(alpha)*y_i*x_i**H

where alpha is a complex scalar, x_i and y_i are vectors, and A_i is an n by n Hermitian matrix for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer pointing to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] specifies the stride between the beginning of one vector (x_i) and the next (x_i+1).
y – [in] device pointer pointing to the first vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] specifies the stride between the beginning of one vector (y_i) and the next (y_i+1).
AP – [inout] device pointer pointing to the first matrix (A_1). Stores the specified triangular portion of each Hermitian matrix A_i. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The lower triangular portion of each A_i will not be touched. if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The upper triangular portion of each A_i will not be touched. Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
lda – [in] [int] specifies the leading dimension of each A_i. Must be at least max(lda, 1).
strideA – [in] [hipblasStride] specifies the stride between the beginning of one matrix (A_i) and the next (A_i+1).
batchCount – [in] [int] number of instances in the batch.

hipblasXhpmv + Batched, StridedBatched #

hipblasStatus_t hipblasChpmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *AP, const hipblasComplex *x, int incx, const hipblasComplex *beta, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZhpmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy)#

BLAS Level 2 API.

hpmv performs the matrix-vector operation

y := alpha*A*x + beta*y

where alpha and beta are scalars, x and y are n element vectors and A is an n by n Hermitian matrix, supplied in packed form (see description below).

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: the upper triangular part of the Hermitian matrix A is supplied in AP. HIPBLAS_FILL_MODE_LOWER: the lower triangular part of the Hermitian matrix A is supplied in AP.
n – [in] [int] the order of the matrix A, must be >= 0.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer storing the packed version of the specified triangular portion of the Hermitian matrix A. Of at least size ((n * (n + 1)) / 2). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of the Hermitian matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (3, 2) (2,-1) (4, 0) (5,-1) –—> [(1,0), (2,1), (4,0), (3,2), (5,-1), (6,0)] (3,-2) (5, 1) (6, 0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of the Hermitian matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (3, 2) (2,-1) (4, 0) (5,-1) –—> [(1,0), (2,-1), (3,-2), (4,0), (5,1), (6,0)] (3,-2) (5, 1) (6, 0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.

hipblasStatus_t hipblasChpmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const AP[], const hipblasComplex *const x[], int incx, const hipblasComplex *beta, hipblasComplex *const y[], int incy, int batchCount)#

hipblasStatus_t hipblasZhpmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const y[], int incy, int batchCount)#

BLAS Level 2 API.

hpmvBatched performs the matrix-vector operation

y_i := alpha*A_i*x_i + beta*y_i

where alpha and beta are scalars, x_i and y_i are n element vectors and A_i is an n by n Hermitian matrix, supplied in packed form (see description below), for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: the upper triangular part of each Hermitian matrix A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: the lower triangular part of each Hermitian matrix A_i is supplied in AP.
n – [in] [int] the order of each matrix A_i.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer of device pointers storing the packed version of the specified triangular portion of each Hermitian matrix A_i. Each A_i is of at least size ((n * (n + 1)) / 2). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that each AP_i contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (3, 2) (2,-1) (4, 0) (5,-1) –—> [(1,0), (2,1), (4,0), (3,2), (5,-1), (6,0)] (3,-2) (5, 1) (6, 0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that each AP_i contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (3, 2) (2,-1) (4, 0) (5,-1) –—> [(1,0), (2,-1), (3,-2), (4,0), (5,1), (6,0)] (3,-2) (5, 1) (6, 0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of y.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasChpmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *AP, hipblasStride strideA, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *beta, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZhpmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, hipblasStride strideA, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

hpmvStridedBatched performs the matrix-vector operation

y_i := alpha*A_i*x_i + beta*y_i

where alpha and beta are scalars, x_i and y_i are n element vectors and A_i is an n by n Hermitian matrix, supplied in packed form (see description below), for each batch in i = [1, batchCount].

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: the upper triangular part of each Hermitian matrix A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: the lower triangular part of each Hermitian matrix A_i is supplied in AP.
n – [in] [int] the order of each matrix A_i.
alpha – [in] device pointer or host pointer to scalar alpha.
AP – [in] device pointer pointing to the beginning of the first matrix (AP_1). Stores the packed version of the specified triangular portion of each Hermitian matrix AP_i of size ((n * (n + 1)) / 2). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that each AP_i contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (3, 2) (2,-1) (4, 0) (5,-1) –—> [(1,0), (2,1), (4,0), (3,2), (5,-1), (6,0)] (3,-2) (5, 1) (6, 0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that each AP_i contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (3, 2) (2,-1) (4, 0) (5,-1) –—> [(1,0), (2,-1), (3,-2), (4,0), (5,1), (6,0)] (3,-2) (5, 1) (6, 0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
strideA – [in] [hipblasStride] stride from the start of one matrix (AP_i) and the next one (AP_i+1).
x – [in] device array pointing to the beginning of the first vector (x_1).
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1).
beta – [in] device pointer or host pointer to scalar beta.
y – [inout] device array pointing to the beginning of the first vector (y_1).
incy – [in] [int] specifies the increment for the elements of y.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1).
batchCount – [in] [int] number of instances in the batch.

hipblasXhpr + Batched, StridedBatched #

hipblasStatus_t hipblasChpr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const hipblasComplex *x, int incx, hipblasComplex *AP)#

hipblasStatus_t hipblasZhpr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *AP)#

BLAS Level 2 API.

hpr performs the matrix-vector operations

A := A + alpha*x*x**H

where alpha is a real scalar, x is a vector, and A is an n by n Hermitian matrix, supplied in packed form.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A is supplied in AP.
n – [in] [int] the number of rows and columns of matrix A, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
AP – [inout] device pointer storing the packed version of the specified triangular portion of the Hermitian matrix A. Of at least size ((n * (n + 1)) / 2). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of the Hermitian matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,1), (3,0), (4,9), (5,3), (6,0)] (4,-9) (5,-3) (6,0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of the Hermitian matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,-1), (4,-9), (3,0), (5,-3), (6,0)] (4,-9) (5,-3) (6,0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.

hipblasStatus_t hipblasChprBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const hipblasComplex *const x[], int incx, hipblasComplex *const AP[], int batchCount)#

hipblasStatus_t hipblasZhprBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const AP[], int batchCount)#

BLAS Level 2 API.

hprBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*x_i**H

where alpha is a real scalar, x_i is a vector, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
AP – [inout] device array of device pointers storing the packed version of the specified triangular portion of each Hermitian matrix A_i of at least size ((n * (n + 1)) / 2). Array is of at least size batchCount. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,1), (3,0), (4,9), (5,3), (6,0)] (4,-9) (5,-3) (6,0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,-1), (4,-9), (3,0), (5,-3), (6,0)] (4,-9) (5,-3) (6,0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasChprStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *AP, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZhprStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *AP, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

hprStridedBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*x_i**H

where alpha is a real scalar, x_i is a vector, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer pointing to the first vector (x_1).
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1).
AP – [inout] device array of device pointers storing the packed version of the specified triangular portion of each Hermitian matrix A_i. Points to the first matrix (A_1). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,1), (3,0), (4,9), (5,3), (6,0)] (4,-9) (5,-3) (6,0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,-1), (4,-9), (3,0), (5,-3), (6,0)] (4,-9) (5,-3) (6,0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
strideA – [in] [hipblasStride] stride from the start of one (A_i) and the next (A_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXhpr2 + Batched, StridedBatched #

hipblasStatus_t hipblasChpr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, const hipblasComplex *y, int incy, hipblasComplex *AP)#

hipblasStatus_t hipblasZhpr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *y, int incy, hipblasDoubleComplex *AP)#

BLAS Level 2 API.

hpr2 performs the matrix-vector operations

A := A + alpha*x*y**H + conj(alpha)*y*x**H

where alpha is a complex scalar, x and y are vectors, and A is an n by n Hermitian matrix, supplied in packed form.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A is supplied in AP.
n – [in] [int] the number of rows and columns of matrix A, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [in] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.
AP – [inout] device pointer storing the packed version of the specified triangular portion of the Hermitian matrix A. Of at least size ((n * (n + 1)) / 2). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of the Hermitian matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,1), (3,0), (4,9), (5,3), (6,0)] (4,-9) (5,-3) (6,0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of the Hermitian matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,-1), (4,-9), (3,0), (5,-3), (6,0)] (4,-9) (5,-3) (6,0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.

hipblasStatus_t hipblasChpr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, const hipblasComplex *const y[], int incy, hipblasComplex *const AP[], int batchCount)#

hipblasStatus_t hipblasZhpr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *const y[], int incy, hipblasDoubleComplex *const AP[], int batchCount)#

BLAS Level 2 API.

hpr2Batched performs the matrix-vector operations

A_i := A_i + alpha*x_i*y_i**H + conj(alpha)*y_i*x_i**H

where alpha is a complex scalar, x_i and y_i are vectors, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [in] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
AP – [inout] device array of device pointers storing the packed version of the specified triangular portion of each Hermitian matrix A_i of at least size ((n * (n + 1)) / 2). Array is of at least size batchCount. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,1), (3,0), (4,9), (5,3), (6,0)] (4,-9) (5,-3) (6,0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,-1), (4,-9), (3,0), (5,-3), (6,0)] (4,-9) (5,-3) (6,0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasChpr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *y, int incy, hipblasStride stridey, hipblasComplex *AP, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZhpr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *y, int incy, hipblasStride stridey, hipblasDoubleComplex *AP, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

hpr2StridedBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*y_i**H + conj(alpha)*y_i*x_i**H

where alpha is a complex scalar, x_i and y_i are vectors, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer pointing to the first vector (x_1).
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1).
y – [in] device pointer pointing to the first vector (y_1).
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1).
AP – [inout] device array of device pointers storing the packed version of the specified triangular portion of each Hermitian matrix A_i. Points to the first matrix (A_1). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,1), (3,0), (4,9), (5,3), (6,0)] (4,-9) (5,-3) (6,0) if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each Hermitian matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 3) (1, 0) (2, 1) (4,9) (2,-1) (3, 0) (5,3) –—> [(1,0), (2,-1), (4,-9), (3,0), (5,-3), (6,0)] (4,-9) (5,-3) (6,0) Note that the imaginary part of the diagonal elements are not accessed and are assumed to be 0.
strideA – [in] [hipblasStride] stride from the start of one (A_i) and the next (A_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXsbmv + Batched, StridedBatched #

hipblasStatus_t hipblasSsbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const float *alpha, const float *AP, int lda, const float *x, int incx, const float *beta, float *y, int incy)#

hipblasStatus_t hipblasDsbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const double *alpha, const double *AP, int lda, const double *x, int incx, const double *beta, double *y, int incy)#

BLAS Level 2 API.

sbmv performs the matrix-vector operation:

y := alpha*A*x + beta*y,

where alpha and beta are scalars, x and y are n element vectors and A should contain an upper or lower triangular n by n symmetric banded matrix.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : s,d

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int]
k – [in] [int] specifies the number of sub- and super-diagonals
alpha – [in] specifies the scalar alpha
AP – [in] pointer storing matrix A on the GPU
lda – [in] [int] specifies the leading dimension of matrix A
x – [in] pointer storing vector x on the GPU
incx – [in] [int] specifies the increment for the elements of x
beta – [in] specifies the scalar beta
y – [out] pointer storing vector y on the GPU
incy – [in] [int] specifies the increment for the elements of y

hipblasStatus_t hipblasSsbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const float *alpha, const float *const AP[], int lda, const float *const x[], int incx, const float *beta, float *y[], int incy, int batchCount)#

hipblasStatus_t hipblasDsbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const double *alpha, const double *const AP[], int lda, const double *const x[], int incx, const double *beta, double *y[], int incy, int batchCount)#

BLAS Level 2 API.

sbmvBatched performs the matrix-vector operation:

y_i := alpha*A_i*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an n by n symmetric banded matrix, for i = 1, …, batchCount. A should contain an upper or lower triangular n by n symmetric banded matrix.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] number of rows and columns of each matrix A_i
k – [in] [int] specifies the number of sub- and super-diagonals
alpha – [in] device pointer or host pointer to scalar alpha
AP – [in] device array of device pointers storing each matrix A_i
lda – [in] [int] specifies the leading dimension of each matrix A_i
x – [in] device array of device pointers storing each vector x_i
incx – [in] [int] specifies the increment for the elements of each vector x_i
beta – [in] device pointer or host pointer to scalar beta
y – [out] device array of device pointers storing each vector y_i
incy – [in] [int] specifies the increment for the elements of each vector y_i
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasSsbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *x, int incx, hipblasStride stridex, const float *beta, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDsbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, int k, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *x, int incx, hipblasStride stridex, const double *beta, double *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

sbmvStridedBatched performs the matrix-vector operation:

y_i := alpha*A_i*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an n by n symmetric banded matrix, for i = 1, …, batchCount. A should contain an upper or lower triangular n by n symmetric banded matrix.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] number of rows and columns of each matrix A_i
k – [in] [int] specifies the number of sub- and super-diagonals
alpha – [in] device pointer or host pointer to scalar alpha
AP – [in] Device pointer to the first matrix A_1 on the GPU
lda – [in] [int] specifies the leading dimension of each matrix A_i
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
x – [in] Device pointer to the first vector x_1 on the GPU
incx – [in] [int] specifies the increment for the elements of each vector x_i
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stridex, however the user should take care to ensure that stridex is of appropriate size. This typically means stridex >= n * incx. stridex should be non zero.
beta – [in] device pointer or host pointer to scalar beta
y – [out] Device pointer to the first vector y_1 on the GPU
incy – [in] [int] specifies the increment for the elements of each vector y_i
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1). There are no restrictions placed on stridey, however the user should take care to ensure that stridey is of appropriate size. This typically means stridey >= n * incy. stridey should be non zero.
batchCount – [in] [int] number of instances in the batch

hipblasXspmv + Batched, StridedBatched #

hipblasStatus_t hipblasSspmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *AP, const float *x, int incx, const float *beta, float *y, int incy)#

hipblasStatus_t hipblasDspmv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *AP, const double *x, int incx, const double *beta, double *y, int incy)#

BLAS Level 2 API.

spmv performs the matrix-vector operation:

y := alpha*A*x + beta*y,

where alpha and beta are scalars, x and y are n element vectors and A should contain an upper or lower triangular n by n packed symmetric matrix.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : s,d

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int]
alpha – [in] specifies the scalar alpha
AP – [in] pointer storing matrix A on the GPU
x – [in] pointer storing vector x on the GPU
incx – [in] [int] specifies the increment for the elements of x
beta – [in] specifies the scalar beta
y – [out] pointer storing vector y on the GPU
incy – [in] [int] specifies the increment for the elements of y

hipblasStatus_t hipblasSspmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *const AP[], const float *const x[], int incx, const float *beta, float *y[], int incy, int batchCount)#

hipblasStatus_t hipblasDspmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *const AP[], const double *const x[], int incx, const double *beta, double *y[], int incy, int batchCount)#

BLAS Level 2 API.

spmvBatched performs the matrix-vector operation:

y_i := alpha*AP_i*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an n by n symmetric matrix, for i = 1, …, batchCount. A should contain an upper or lower triangular n by n packed symmetric matrix.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] number of rows and columns of each matrix A_i
alpha – [in] device pointer or host pointer to scalar alpha
AP – [in] device array of device pointers storing each matrix A_i
x – [in] device array of device pointers storing each vector x_i
incx – [in] [int] specifies the increment for the elements of each vector x_i
beta – [in] device pointer or host pointer to scalar beta
y – [out] device array of device pointers storing each vector y_i
incy – [in] [int] specifies the increment for the elements of each vector y_i
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasSspmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *AP, hipblasStride strideA, const float *x, int incx, hipblasStride stridex, const float *beta, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDspmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *AP, hipblasStride strideA, const double *x, int incx, hipblasStride stridex, const double *beta, double *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

spmvStridedBatched performs the matrix-vector operation:

y_i := alpha*A_i*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an n by n symmetric matrix, for i = 1, …, batchCount. A should contain an upper or lower triangular n by n packed symmetric matrix.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] number of rows and columns of each matrix A_i
alpha – [in] device pointer or host pointer to scalar alpha
AP – [in] Device pointer to the first matrix A_1 on the GPU
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
x – [in] Device pointer to the first vector x_1 on the GPU
incx – [in] [int] specifies the increment for the elements of each vector x_i
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stridex, however the user should take care to ensure that stridex is of appropriate size. This typically means stridex >= n * incx. stridex should be non zero.
beta – [in] device pointer or host pointer to scalar beta
y – [out] Device pointer to the first vector y_1 on the GPU
incy – [in] [int] specifies the increment for the elements of each vector y_i
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1). There are no restrictions placed on stridey, however the user should take care to ensure that stridey is of appropriate size. This typically means stridey >= n * incy. stridey should be non zero.
batchCount – [in] [int] number of instances in the batch

hipblasXspr + Batched, StridedBatched #

hipblasStatus_t hipblasSspr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, float *AP)#

hipblasStatus_t hipblasDspr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, double *AP)#

hipblasStatus_t hipblasCspr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasComplex *AP)#

hipblasStatus_t hipblasZspr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *AP)#

BLAS Level 2 API.

spr performs the matrix-vector operations

A := A + alpha*x*x**T

where alpha is a scalar, x is a vector, and A is an n by n symmetric matrix, supplied in packed form.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A is supplied in AP.
n – [in] [int] the number of rows and columns of matrix A, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
AP – [inout] device pointer storing the packed version of the specified triangular portion of the symmetric matrix A. Of at least size ((n * (n + 1)) / 2). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of the symmetric matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 4) 1 2 4 7 2 3 5 8 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 4 5 6 9 7 8 9 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of the symmetric matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 4) 1 2 3 4 2 5 6 7 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 3 6 8 9 4 7 9 0

hipblasStatus_t hipblasSsprBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *const x[], int incx, float *const AP[], int batchCount)#

hipblasStatus_t hipblasDsprBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *const x[], int incx, double *const AP[], int batchCount)#

hipblasStatus_t hipblasCsprBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, hipblasComplex *const AP[], int batchCount)#

hipblasStatus_t hipblasZsprBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const AP[], int batchCount)#

BLAS Level 2 API.

sprBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*x_i**T

where alpha is a scalar, x_i is a vector, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
AP – [inout] device array of device pointers storing the packed version of the specified triangular portion of each symmetric matrix A_i of at least size ((n * (n + 1)) / 2). Array is of at least size batchCount. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 4) 1 2 4 7 2 3 5 8 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 4 5 6 9 7 8 9 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 4) 1 2 3 4 2 5 6 7 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 3 6 8 9 4 7 9 0
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSsprStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, hipblasStride stridex, float *AP, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasDsprStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, hipblasStride stridex, double *AP, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasCsprStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *AP, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZsprStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *AP, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

sprStridedBatched performs the matrix-vector operations

A_i := A_i + alpha*x_i*x_i**T

where alpha is a scalar, x_i is a vector, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer pointing to the first vector (x_1).
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1).
AP – [inout] device pointer storing the packed version of the specified triangular portion of each symmetric matrix A_i. Points to the first A_1. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 4) 1 2 4 7 2 3 5 8 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 4 5 6 9 7 8 9 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(2) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 4) 1 2 3 4 2 5 6 7 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 3 6 8 9 4 7 9 0
strideA – [in] [hipblasStride] stride from the start of one (A_i) and the next (A_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXspr2 + Batched, StridedBatched #

hipblasStatus_t hipblasSspr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, const float *y, int incy, float *AP)#

hipblasStatus_t hipblasDspr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, const double *y, int incy, double *AP)#

BLAS Level 2 API.

spr2 performs the matrix-vector operation

A := A + alpha*x*y**T + alpha*y*x**T

where alpha is a scalar, x and y are vectors, and A is an n by n symmetric matrix, supplied in packed form.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : s,d

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of A is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of A is supplied in AP.
n – [in] [int] the number of rows and columns of matrix A, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [in] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.
AP – [inout] device pointer storing the packed version of the specified triangular portion of the symmetric matrix A. Of at least size ((n * (n + 1)) / 2). if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of the symmetric matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 4) 1 2 4 7 2 3 5 8 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 4 5 6 9 7 8 9 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of the symmetric matrix A is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(n) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 4) 1 2 3 4 2 5 6 7 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 3 6 8 9 4 7 9 0

hipblasStatus_t hipblasSspr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *const x[], int incx, const float *const y[], int incy, float *const AP[], int batchCount)#

hipblasStatus_t hipblasDspr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *const x[], int incx, const double *const y[], int incy, double *const AP[], int batchCount)#

BLAS Level 2 API.

spr2Batched performs the matrix-vector operation

A_i := A_i + alpha*x_i*y_i**T + alpha*y_i*x_i**T

where alpha is a scalar, x_i and y_i are vectors, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [in] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
AP – [inout] device array of device pointers storing the packed version of the specified triangular portion of each symmetric matrix A_i of at least size ((n * (n + 1)) / 2). Array is of at least size batchCount. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 4) 1 2 4 7 2 3 5 8 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 4 5 6 9 7 8 9 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(n) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 4) 1 2 3 4 2 5 6 7 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 3 6 8 9 4 7 9 0
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSspr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, hipblasStride stridex, const float *y, int incy, hipblasStride stridey, float *AP, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasDspr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, hipblasStride stridex, const double *y, int incy, hipblasStride stridey, double *AP, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

spr2StridedBatched performs the matrix-vector operation

A_i := A_i + alpha*x_i*y_i**T + alpha*y_i*x_i**T

where alpha is a scalar, x_i amd y_i are vectors, and A_i is an n by n symmetric matrix, supplied in packed form, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ HIPBLAS_FILL_MODE_UPPER: The upper triangular part of each A_i is supplied in AP. HIPBLAS_FILL_MODE_LOWER: The lower triangular part of each A_i is supplied in AP.
n – [in] [int] the number of rows and columns of each matrix A_i, must be at least 0.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer pointing to the first vector (x_1).
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1).
y – [in] device pointer pointing to the first vector (y_1).
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1).
AP – [inout] device pointer storing the packed version of the specified triangular portion of each symmetric matrix A_i. Points to the first A_1. if uplo == HIPBLAS_FILL_MODE_UPPER: The upper triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(0,1) AP(2) = A(1,1), etc. Ex: (HIPBLAS_FILL_MODE_UPPER; n = 4) 1 2 4 7 2 3 5 8 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 4 5 6 9 7 8 9 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The lower triangular portion of each symmetric matrix A_i is supplied. The matrix is compacted so that AP contains the triangular portion column-by-column so that: AP(0) = A(0,0) AP(1) = A(1,0) AP(n) = A(2,1), etc. Ex: (HIPBLAS_FILL_MODE_LOWER; n = 4) 1 2 3 4 2 5 6 7 –—> [1, 2, 3, 4, 5, 6, 7, 8, 9, 0] 3 6 8 9 4 7 9 0
strideA – [in] [hipblasStride] stride from the start of one (A_i) and the next (A_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXsymv + Batched, StridedBatched #

hipblasStatus_t hipblasSsymv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *AP, int lda, const float *x, int incx, const float *beta, float *y, int incy)#

hipblasStatus_t hipblasDsymv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *AP, int lda, const double *x, int incx, const double *beta, double *y, int incy)#

hipblasStatus_t hipblasCsymv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *x, int incx, const hipblasComplex *beta, hipblasComplex *y, int incy)#

hipblasStatus_t hipblasZsymv(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy)#

BLAS Level 2 API.

symv performs the matrix-vector operation:

y := alpha*A*x + beta*y,

where alpha and beta are scalars, x and y are n element vectors and A should contain an upper or lower triangular n by n symmetric matrix.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int]
alpha – [in] specifies the scalar alpha
AP – [in] pointer storing matrix A on the GPU
lda – [in] [int] specifies the leading dimension of A
x – [in] pointer storing vector x on the GPU
incx – [in] [int] specifies the increment for the elements of x
beta – [in] specifies the scalar beta
y – [out] pointer storing vector y on the GPU
incy – [in] [int] specifies the increment for the elements of y

hipblasStatus_t hipblasSsymvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *const AP[], int lda, const float *const x[], int incx, const float *beta, float *y[], int incy, int batchCount)#

hipblasStatus_t hipblasDsymvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *const AP[], int lda, const double *const x[], int incx, const double *beta, double *y[], int incy, int batchCount)#

hipblasStatus_t hipblasCsymvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const x[], int incx, const hipblasComplex *beta, hipblasComplex *y[], int incy, int batchCount)#

hipblasStatus_t hipblasZsymvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y[], int incy, int batchCount)#

BLAS Level 2 API.

symvBatched performs the matrix-vector operation:

y_i := alpha*A_i*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an n by n symmetric matrix, for i = 1, …, batchCount. A a should contain an upper or lower triangular symmetric matrix and the opposing triangular part of A is not referenced

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] number of rows and columns of each matrix A_i
alpha – [in] device pointer or host pointer to scalar alpha
AP – [in] device array of device pointers storing each matrix A_i
lda – [in] [int] specifies the leading dimension of each matrix A_i
x – [in] device array of device pointers storing each vector x_i
incx – [in] [int] specifies the increment for the elements of each vector x_i
beta – [in] device pointer or host pointer to scalar beta
y – [out] device array of device pointers storing each vector y_i
incy – [in] [int] specifies the increment for the elements of each vector y_i
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasSsymvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *x, int incx, hipblasStride stridex, const float *beta, float *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasDsymvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *x, int incx, hipblasStride stridex, const double *beta, double *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasCsymvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *beta, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#

hipblasStatus_t hipblasZsymvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *beta, hipblasDoubleComplex *y, int incy, hipblasStride stridey, int batchCount)#

BLAS Level 2 API.

symvStridedBatched performs the matrix-vector operation:

y_i := alpha*A_i*x_i + beta*y_i,

where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an n by n symmetric matrix, for i = 1, …, batchCount. A a should contain an upper or lower triangular symmetric matrix and the opposing triangular part of A is not referenced

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] number of rows and columns of each matrix A_i
alpha – [in] device pointer or host pointer to scalar alpha
AP – [in] Device pointer to the first matrix A_1 on the GPU
lda – [in] [int] specifies the leading dimension of each matrix A_i
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
x – [in] Device pointer to the first vector x_1 on the GPU
incx – [in] [int] specifies the increment for the elements of each vector x_i
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stridex, however the user should take care to ensure that stridex is of appropriate size. This typically means stridex >= n * incx. stridex should be non zero.
beta – [in] device pointer or host pointer to scalar beta
y – [out] Device pointer to the first vector y_1 on the GPU
incy – [in] [int] specifies the increment for the elements of each vector y_i
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1). There are no restrictions placed on stridey, however the user should take care to ensure that stridey is of appropriate size. This typically means stridey >= n * incy. stridey should be non zero.
batchCount – [in] [int] number of instances in the batch

hipblasXsyr + Batched, StridedBatched #

hipblasStatus_t hipblasSsyr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, float *AP, int lda)#

hipblasStatus_t hipblasDsyr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, double *AP, int lda)#

hipblasStatus_t hipblasCsyr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasComplex *AP, int lda)#

hipblasStatus_t hipblasZsyr(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *AP, int lda)#

BLAS Level 2 API.

syr performs the matrix-vector operations

A := A + alpha*x*x**T

where alpha is a scalar, x is a vector, and A is an n by n symmetric matrix.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] the number of rows and columns of matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
AP – [inout] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.

hipblasStatus_t hipblasSsyrBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *const x[], int incx, float *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasDsyrBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *const x[], int incx, double *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasCsyrBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, hipblasComplex *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasZsyrBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const AP[], int lda, int batchCount)#

BLAS Level 2 API.

syrBatched performs a batch of matrix-vector operations

A[i] := A[i] + alpha*x[i]*x[i]**T

where alpha is a scalar, x is an array of vectors, and A is an array of n by n symmetric matrices, for i = 1 , … , batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] the number of rows and columns of matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
AP – [inout] device array of device pointers storing each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i.
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasSsyrStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, hipblasStride stridex, float *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasDsyrStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, hipblasStride stridex, double *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasCsyrStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZsyrStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *AP, int lda, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

syrStridedBatched performs the matrix-vector operations

A[i] := A[i] + alpha*x[i]*x[i]**T

where alpha is a scalar, vectors, and A is an array of n by n symmetric matrices, for i = 1 , … , batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] the number of rows and columns of each matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] specifies the pointer increment between vectors (x_i) and (x_i+1).
AP – [inout] device pointer to the first matrix A_1.
lda – [in] [int] specifies the leading dimension of each A_i.
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
batchCount – [in] [int] number of instances in the batch

hipblasXsyr2 + Batched, StridedBatched #

hipblasStatus_t hipblasSsyr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, const float *y, int incy, float *AP, int lda)#

hipblasStatus_t hipblasDsyr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, const double *y, int incy, double *AP, int lda)#

hipblasStatus_t hipblasCsyr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, const hipblasComplex *y, int incy, hipblasComplex *AP, int lda)#

hipblasStatus_t hipblasZsyr2(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, const hipblasDoubleComplex *y, int incy, hipblasDoubleComplex *AP, int lda)#

BLAS Level 2 API.

syr2 performs the matrix-vector operations

A := A + alpha*x*y**T + alpha*y*x**T

where alpha is a scalar, x and y are vectors, and A is an n by n symmetric matrix.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] the number of rows and columns of matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [in] device pointer storing vector y.
incy – [in] [int] specifies the increment for the elements of y.
AP – [inout] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.

hipblasStatus_t hipblasSsyr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *const x[], int incx, const float *const y[], int incy, float *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasDsyr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *const x[], int incx, const double *const y[], int incy, double *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasCsyr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *const x[], int incx, const hipblasComplex *const y[], int incy, hipblasComplex *const AP[], int lda, int batchCount)#

hipblasStatus_t hipblasZsyr2Batched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const x[], int incx, const hipblasDoubleComplex *const y[], int incy, hipblasDoubleComplex *const AP[], int lda, int batchCount)#

BLAS Level 2 API.

syr2Batched performs a batch of matrix-vector operations

A[i] := A[i] + alpha*x[i]*y[i]**T + alpha*y[i]*x[i]**T

where alpha is a scalar, x[i] and y[i] are vectors, and A[i] is a n by n symmetric matrix, for i = 1 , … , batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] the number of rows and columns of matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [in] device array of device pointers storing each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
AP – [inout] device array of device pointers storing each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i.
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasSsyr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const float *alpha, const float *x, int incx, hipblasStride stridex, const float *y, int incy, hipblasStride stridey, float *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasDsyr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const double *alpha, const double *x, int incx, hipblasStride stridex, const double *y, int incy, hipblasStride stridey, double *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasCsyr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasComplex *alpha, const hipblasComplex *x, int incx, hipblasStride stridex, const hipblasComplex *y, int incy, hipblasStride stridey, hipblasComplex *AP, int lda, hipblasStride strideA, int batchCount)#

hipblasStatus_t hipblasZsyr2StridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, const hipblasDoubleComplex *y, int incy, hipblasStride stridey, hipblasDoubleComplex *AP, int lda, hipblasStride strideA, int batchCount)#

BLAS Level 2 API.

syr2StridedBatched the matrix-vector operations

A[i] := A[i] + alpha*x[i]*y[i]**T + alpha*y[i]*x[i]**T

where alpha is a scalar, x[i] and y[i] are vectors, and A[i] is a n by n symmetric matrices, for i = 1 , … , batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
n – [in] [int] the number of rows and columns of each matrix A.
alpha – [in] device pointer or host pointer to scalar alpha.
x – [in] device pointer to the first vector x_1.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] specifies the pointer increment between vectors (x_i) and (x_i+1).
y – [in] device pointer to the first vector y_1.
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] specifies the pointer increment between vectors (y_i) and (y_i+1).
AP – [inout] device pointer to the first matrix A_1.
lda – [in] [int] specifies the leading dimension of each A_i.
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
batchCount – [in] [int] number of instances in the batch

hipblasXtbmv + Batched, StridedBatched #

hipblasStatus_t hipblasStbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const float *AP, int lda, float *x, int incx)#

hipblasStatus_t hipblasDtbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const double *AP, int lda, double *x, int incx)#

hipblasStatus_t hipblasCtbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const hipblasComplex *AP, int lda, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasZtbmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const hipblasDoubleComplex *AP, int lda, hipblasDoubleComplex *x, int incx)#

BLAS Level 2 API.

tbmv performs one of the matrix-vector operations

x := A*x      or
x := A**T*x   or
x := A**H*x,

x is a vectors and A is a banded m by m matrix (see description below).

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper banded triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower banded triangular matrix.
transA – [in] [hipblasOperation_t] indicates whether matrix A is tranposed (conjugated) or not.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: The main diagonal of A is assumed to consist of only 1’s and is not referenced. HIPBLAS_DIAG_NON_UNIT: No assumptions are made of A’s main diagonal.
m – [in] [int] the number of rows and columns of the matrix represented by A.
k – [in] [int] if uplo == HIPBLAS_FILL_MODE_UPPER, k specifies the number of super-diagonals of the matrix A. if uplo == HIPBLAS_FILL_MODE_LOWER, k specifies the number of sub-diagonals of the matrix A. k must satisfy k > 0 && k < lda.
AP – [in] device pointer storing banded triangular matrix A. if uplo == HIPBLAS_FILL_MODE_UPPER: The matrix represented is an upper banded triangular matrix with the main diagonal and k super-diagonals, everything else can be assumed to be 0. The matrix is compacted so that the main diagonal resides on the k’th row, the first super diagonal resides on the RHS of the k-1’th row, etc, with the k’th diagonal on the RHS of the 0’th row. Ex: (HIPBLAS_FILL_MODE_UPPER; m = 5; k = 2) 1 6 9 0 0 0 0 9 8 7 0 2 7 8 0 0 6 7 8 9 0 0 3 8 7 -—> 1 2 3 4 5 0 0 0 4 9 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The matrix represnted is a lower banded triangular matrix with the main diagonal and k sub-diagonals, everything else can be assumed to be 0. The matrix is compacted so that the main diagonal resides on the 0’th row, working up to the k’th diagonal residing on the LHS of the k’th row. Ex: (HIPBLAS_FILL_MODE_LOWER; m = 5; k = 2) 1 0 0 0 0 1 2 3 4 5 6 2 0 0 0 6 7 8 9 0 9 7 3 0 0 -—> 9 8 7 0 0 0 8 8 4 0 0 0 0 0 0 0 0 7 9 5 0 0 0 0 0
lda – [in] [int] specifies the leading dimension of A. lda must satisfy lda > k.
x – [inout] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.

hipblasStatus_t hipblasStbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const float *const AP[], int lda, float *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasDtbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const double *const AP[], int lda, double *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCtbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const hipblasComplex *const AP[], int lda, hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZtbmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const hipblasDoubleComplex *const AP[], int lda, hipblasDoubleComplex *const x[], int incx, int batchCount)#

BLAS Level 2 API.

tbmvBatched performs one of the matrix-vector operations

x_i := A_i*x_i      or
x_i := A_i**T*x_i   or
x_i := A_i**H*x_i,

where (A_i, x_i) is the i-th instance of the batch. x_i is a vector and A_i is an m by m matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper banded triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower banded triangular matrix.
transA – [in] [hipblasOperation_t] indicates whether each matrix A_i is tranposed (conjugated) or not.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: The main diagonal of each A_i is assumed to consist of only 1’s and is not referenced. HIPBLAS_DIAG_NON_UNIT: No assumptions are made of each A_i’s main diagonal.
m – [in] [int] the number of rows and columns of the matrix represented by each A_i.
k – [in] [int] if uplo == HIPBLAS_FILL_MODE_UPPER, k specifies the number of super-diagonals of each matrix A_i. if uplo == HIPBLAS_FILL_MODE_LOWER, k specifies the number of sub-diagonals of each matrix A_i. k must satisfy k > 0 && k < lda.
AP – [in] device array of device pointers storing each banded triangular matrix A_i. if uplo == HIPBLAS_FILL_MODE_UPPER: The matrix represented is an upper banded triangular matrix with the main diagonal and k super-diagonals, everything else can be assumed to be 0. The matrix is compacted so that the main diagonal resides on the k’th row, the first super diagonal resides on the RHS of the k-1’th row, etc, with the k’th diagonal on the RHS of the 0’th row. Ex: (HIPBLAS_FILL_MODE_UPPER; m = 5; k = 2) 1 6 9 0 0 0 0 9 8 7 0 2 7 8 0 0 6 7 8 9 0 0 3 8 7 -—> 1 2 3 4 5 0 0 0 4 9 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The matrix represnted is a lower banded triangular matrix with the main diagonal and k sub-diagonals, everything else can be assumed to be 0. The matrix is compacted so that the main diagonal resides on the 0’th row, working up to the k’th diagonal residing on the LHS of the k’th row. Ex: (HIPBLAS_FILL_MODE_LOWER; m = 5; k = 2) 1 0 0 0 0 1 2 3 4 5 6 2 0 0 0 6 7 8 9 0 9 7 3 0 0 -—> 9 8 7 0 0 0 8 8 4 0 0 0 0 0 0 0 0 7 9 5 0 0 0 0 0
lda – [in] [int] specifies the leading dimension of each A_i. lda must satisfy lda > k.
x – [inout] device array of device pointer storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasStbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const float *AP, int lda, hipblasStride strideA, float *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasDtbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const double *AP, int lda, hipblasStride strideA, double *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCtbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const hipblasComplex *AP, int lda, hipblasStride strideA, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZtbmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int k, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

BLAS Level 2 API.

tbmvStridedBatched performs one of the matrix-vector operations

x_i := A_i*x_i      or
x_i := A_i**T*x_i   or
x_i := A_i**H*x_i,

where (A_i, x_i) is the i-th instance of the batch. x_i is a vector and A_i is an m by m matrix, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper banded triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower banded triangular matrix.
transA – [in] [hipblasOperation_t] indicates whether each matrix A_i is tranposed (conjugated) or not.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: The main diagonal of each A_i is assumed to consist of only 1’s and is not referenced. HIPBLAS_DIAG_NON_UNIT: No assumptions are made of each A_i’s main diagonal.
m – [in] [int] the number of rows and columns of the matrix represented by each A_i.
k – [in] [int] if uplo == HIPBLAS_FILL_MODE_UPPER, k specifies the number of super-diagonals of each matrix A_i. if uplo == HIPBLAS_FILL_MODE_LOWER, k specifies the number of sub-diagonals of each matrix A_i. k must satisfy k > 0 && k < lda.
AP – [in] device array to the first matrix A_i of the batch. Stores each banded triangular matrix A_i. if uplo == HIPBLAS_FILL_MODE_UPPER: The matrix represented is an upper banded triangular matrix with the main diagonal and k super-diagonals, everything else can be assumed to be 0. The matrix is compacted so that the main diagonal resides on the k’th row, the first super diagonal resides on the RHS of the k-1’th row, etc, with the k’th diagonal on the RHS of the 0’th row. Ex: (HIPBLAS_FILL_MODE_UPPER; m = 5; k = 2) 1 6 9 0 0 0 0 9 8 7 0 2 7 8 0 0 6 7 8 9 0 0 3 8 7 -—> 1 2 3 4 5 0 0 0 4 9 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 if uplo == HIPBLAS_FILL_MODE_LOWER: The matrix represnted is a lower banded triangular matrix with the main diagonal and k sub-diagonals, everything else can be assumed to be 0. The matrix is compacted so that the main diagonal resides on the 0’th row, working up to the k’th diagonal residing on the LHS of the k’th row. Ex: (HIPBLAS_FILL_MODE_LOWER; m = 5; k = 2) 1 0 0 0 0 1 2 3 4 5 6 2 0 0 0 6 7 8 9 0 9 7 3 0 0 -—> 9 8 7 0 0 0 8 8 4 0 0 0 0 0 0 0 0 7 9 5 0 0 0 0 0
lda – [in] [int] specifies the leading dimension of each A_i. lda must satisfy lda > k.
strideA – [in] [hipblasStride] stride from the start of one A_i matrix to the next A_(i + 1).
x – [inout] device array to the first vector x_i of the batch.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one x_i matrix to the next x_(i + 1).
batchCount – [in] [int] number of instances in the batch.

hipblasXtbsv + Batched, StridedBatched #

hipblasStatus_t hipblasStbsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const float *AP, int lda, float *x, int incx)#

hipblasStatus_t hipblasDtbsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const double *AP, int lda, double *x, int incx)#

hipblasStatus_t hipblasCtbsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const hipblasComplex *AP, int lda, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasZtbsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const hipblasDoubleComplex *AP, int lda, hipblasDoubleComplex *x, int incx)#

BLAS Level 2 API.

tbsv solves

 A*x = b or A**T*x = b or A**H*x = b,

where x and b are vectors and A is a banded triangular matrix.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: Solves A*x = b HIPBLAS_OP_T: Solves A**T*x = b HIPBLAS_OP_C: Solves A**H*x = b
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular (i.e. the diagonal elements of A are not used in computations). HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
n – [in] [int] n specifies the number of rows of b. n >= 0.
k – [in] [int] if(uplo == HIPBLAS_FILL_MODE_UPPER) k specifies the number of super-diagonals of A. if(uplo == HIPBLAS_FILL_MODE_LOWER) k specifies the number of sub-diagonals of A. k >= 0.
AP – [in] device pointer storing the matrix A in banded format.
lda – [in] [int] specifies the leading dimension of A. lda >= (k + 1).
x – [inout] device pointer storing input vector b. Overwritten by the output vector x.
incx – [in] [int] specifies the increment for the elements of x.

hipblasStatus_t hipblasStbsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const float *const AP[], int lda, float *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasDtbsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const double *const AP[], int lda, double *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCtbsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const hipblasComplex *const AP[], int lda, hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZtbsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const hipblasDoubleComplex *const AP[], int lda, hipblasDoubleComplex *const x[], int incx, int batchCount)#

BLAS Level 2 API.

tbsvBatched solves

 A_i*x_i = b_i or A_i**T*x_i = b_i or A_i**H*x_i = b_i,

where x_i and b_i are vectors and A_i is a banded triangular matrix, for i = [1, batchCount].

The input vectors b_i are overwritten by the output vectors x_i.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: Solves A_i*x_i = b_i HIPBLAS_OP_T: Solves A_i**T*x_i = b_i HIPBLAS_OP_C: Solves A_i**H*x_i = b_i
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular (i.e. the diagonal elements of each A_i are not used in computations). HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
n – [in] [int] n specifies the number of rows of each b_i. n >= 0.
k – [in] [int] if(uplo == HIPBLAS_FILL_MODE_UPPER) k specifies the number of super-diagonals of each A_i. if(uplo == HIPBLAS_FILL_MODE_LOWER) k specifies the number of sub-diagonals of each A_i. k >= 0.
AP – [in] device vector of device pointers storing each matrix A_i in banded format.
lda – [in] [int] specifies the leading dimension of each A_i. lda >= (k + 1).
x – [inout] device vector of device pointers storing each input vector b_i. Overwritten by each output vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasStbsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const float *AP, int lda, hipblasStride strideA, float *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasDtbsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const double *AP, int lda, hipblasStride strideA, double *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCtbsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const hipblasComplex *AP, int lda, hipblasStride strideA, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZtbsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int n, int k, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

BLAS Level 2 API.

tbsvStridedBatched solves

 A_i*x_i = b_i or A_i**T*x_i = b_i or A_i**H*x_i = b_i,

where x_i and b_i are vectors and A_i is a banded triangular matrix, for i = [1, batchCount].

The input vectors b_i are overwritten by the output vectors x_i.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: Solves A_i*x_i = b_i HIPBLAS_OP_T: Solves A_i**T*x_i = b_i HIPBLAS_OP_C: Solves A_i**H*x_i = b_i
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular (i.e. the diagonal elements of each A_i are not used in computations). HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
n – [in] [int] n specifies the number of rows of each b_i. n >= 0.
k – [in] [int] if(uplo == HIPBLAS_FILL_MODE_UPPER) k specifies the number of super-diagonals of each A_i. if(uplo == HIPBLAS_FILL_MODE_LOWER) k specifies the number of sub-diagonals of each A_i. k >= 0.
AP – [in] device pointer pointing to the first banded matrix A_1.
lda – [in] [int] specifies the leading dimension of each A_i. lda >= (k + 1).
strideA – [in] [hipblasStride] specifies the distance between the start of one matrix (A_i) and the next (A_i+1).
x – [inout] device pointer pointing to the first input vector b_1. Overwritten by output vectors x.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] specifies the distance between the start of one vector (x_i) and the next (x_i+1).
batchCount – [in] [int] number of instances in the batch.

hipblasXtpmv + Batched, StridedBatched #

hipblasStatus_t hipblasStpmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, float *x, int incx)#

hipblasStatus_t hipblasDtpmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, double *x, int incx)#

hipblasStatus_t hipblasCtpmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasZtpmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, hipblasDoubleComplex *x, int incx)#

BLAS Level 2 API.

tpmv performs one of the matrix-vector operations

 x = A*x or x = A**T*x,

where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix, supplied in the pack form.

The vector x is overwritten.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of A. m >= 0.
AP – [in] device pointer storing matrix A, of dimension at leat ( m * ( m + 1 ) / 2 ). Before entry with uplo = HIPBLAS_FILL_MODE_UPPER, the array A must contain the upper triangular matrix packed sequentially, column by column, so that A[0] contains a_{0,0}, A[1] and A[2] contain a_{0,1} and a_{1, 1} respectively, and so on. Before entry with uplo = HIPBLAS_FILL_MODE_LOWER, the array A must contain the lower triangular matrix packed sequentially, column by column, so that A[0] contains a_{0,0}, A[1] and A[2] contain a_{1,0} and a_{2,0} respectively, and so on. Note that when DIAG = HIPBLAS_DIAG_UNIT, the diagonal elements of A are not referenced, but are assumed to be unity.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x. incx must not be zero.

hipblasStatus_t hipblasStpmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *const AP[], float *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasDtpmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *const AP[], double *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCtpmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *const AP[], hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZtpmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *const AP[], hipblasDoubleComplex *const x[], int incx, int batchCount)#

BLAS Level 2 API.

tpmvBatched performs one of the matrix-vector operations

 x_i = A_i*x_i or x_i = A**T*x_i, 0 \le i < batchCount

where x_i is an n element vector and A_i is an n by n (unit, or non-unit, upper or lower triangular matrix)

The vectors x_i are overwritten.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of matrices A_i. m >= 0.
AP – [in] device pointer storing pointer of matrices A_i, of dimension ( lda, m )
x – [in] device pointer storing vectors x_i.
incx – [in] [int] specifies the increment for the elements of vectors x_i.
batchCount – [in] [int] The number of batched matrices/vectors.

hipblasStatus_t hipblasStpmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, hipblasStride strideA, float *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasDtpmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, hipblasStride strideA, double *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCtpmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, hipblasStride strideA, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZtpmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, hipblasStride strideA, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

BLAS Level 2 API.

tpmvStridedBatched performs one of the matrix-vector operations

 x_i = A_i*x_i or x_i = A**T*x_i, 0 \le i < batchCount

where x_i is an n element vector and A_i is an n by n (unit, or non-unit, upper or lower triangular matrix) with strides specifying how to retrieve $x_i$ (resp. $A_i$) from $x_{i-1}$ (resp. $A_i$).

The vectors x_i are overwritten.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of matrices A_i. m >= 0.
AP – [in] device pointer of the matrix A_0, of dimension ( lda, m )
strideA – [in] [hipblasStride] stride from the start of one A_i matrix to the next A_{i + 1}
x – [in] device pointer storing the vector x_0.
incx – [in] [int] specifies the increment for the elements of one vector x.
stridex – [in] [hipblasStride] stride from the start of one x_i vector to the next x_{i + 1}
batchCount – [in] [int] The number of batched matrices/vectors.

hipblasXtpsv + Batched, StridedBatched #

hipblasStatus_t hipblasStpsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, float *x, int incx)#

hipblasStatus_t hipblasDtpsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, double *x, int incx)#

hipblasStatus_t hipblasCtpsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasZtpsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, hipblasDoubleComplex *x, int incx)#

BLAS Level 2 API.

tpsv solves

 A*x = b or A**T*x = b, or A**H*x = b,

where x and b are vectors and A is a triangular matrix stored in the packed format.

The input vector b is overwritten by the output vector x.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: Solves A*x = b HIPBLAS_OP_T: Solves A**T*x = b HIPBLAS_OP_C: Solves A**H*x = b
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular (i.e. the diagonal elements of A are not used in computations). HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of b. m >= 0.
AP – [in] device pointer storing the packed version of matrix A, of dimension >= (n * (n + 1) / 2)
x – [inout] device pointer storing vector b on input, overwritten by x on output.
incx – [in] [int] specifies the increment for the elements of x.

hipblasStatus_t hipblasStpsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *const AP[], float *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasDtpsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *const AP[], double *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCtpsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *const AP[], hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZtpsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *const AP[], hipblasDoubleComplex *const x[], int incx, int batchCount)#

BLAS Level 2 API.

tpsvBatched solves

 A_i*x_i = b_i or A_i**T*x_i = b_i, or A_i**H*x_i = b_i,

where x_i and b_i are vectors and A_i is a triangular matrix stored in the packed format, for i in [1, batchCount].

The input vectors b_i are overwritten by the output vectors x_i.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: Solves A*x = b HIPBLAS_OP_T: Solves A**T*x = b HIPBLAS_OP_C: Solves A**H*x = b
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular (i.e. the diagonal elements of each A_i are not used in computations). HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of each b_i. m >= 0.
AP – [in] device array of device pointers storing the packed versions of each matrix A_i, of dimension >= (n * (n + 1) / 2)
x – [inout] device array of device pointers storing each input vector b_i, overwritten by x_i on output.
incx – [in] [int] specifies the increment for the elements of each x_i.
batchCount – [in] [int] specifies the number of instances in the batch.

hipblasStatus_t hipblasStpsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, hipblasStride strideA, float *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasDtpsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, hipblasStride strideA, double *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCtpsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, hipblasStride strideA, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZtpsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, hipblasStride strideA, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

BLAS Level 2 API.

tpsvStridedBatched solves

 A_i*x_i = b_i or A_i**T*x_i = b_i, or A_i**H*x_i = b_i,

where x_i and b_i are vectors and A_i is a triangular matrix stored in the packed format, for i in [1, batchCount].

The input vectors b_i are overwritten by the output vectors x_i.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: Solves A*x = b HIPBLAS_OP_T: Solves A**T*x = b HIPBLAS_OP_C: Solves A**H*x = b
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular (i.e. the diagonal elements of each A_i are not used in computations). HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of each b_i. m >= 0.
AP – [in] device pointer pointing to the first packed matrix A_1, of dimension >= (n * (n + 1) / 2)
strideA – [in] [hipblasStride] stride from the beginning of one packed matrix (AP_i) and the next (AP_i+1).
x – [inout] device pointer pointing to the first input vector b_1. Overwritten by each x_i on output.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the beginning of one vector (x_i) and the next (x_i+1).
batchCount – [in] [int] specifies the number of instances in the batch.

hipblasXtrmv + Batched, StridedBatched #

hipblasStatus_t hipblasStrmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, int lda, float *x, int incx)#

hipblasStatus_t hipblasDtrmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, int lda, double *x, int incx)#

hipblasStatus_t hipblasCtrmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, int lda, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasZtrmv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, int lda, hipblasDoubleComplex *x, int incx)#

BLAS Level 2 API.

trmv performs one of the matrix-vector operations

 x = A*x or x = A**T*x,

where x is an n element vector and A is an n by n unit, or non-unit, upper or lower triangular matrix.

The vector x is overwritten.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of A. m >= 0.
AP – [in] device pointer storing matrix A, of dimension ( lda, m )
lda – [in] [int] specifies the leading dimension of A. lda = max( 1, m ).
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.

hipblasStatus_t hipblasStrmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *const AP[], int lda, float *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasDtrmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *const AP[], int lda, double *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCtrmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *const AP[], int lda, hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZtrmvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *const AP[], int lda, hipblasDoubleComplex *const x[], int incx, int batchCount)#

BLAS Level 2 API.

trmvBatched performs one of the matrix-vector operations

 x_i = A_i*x_i or x_i = A**T*x_i, 0 \le i < batchCount

where x_i is an n element vector and A_i is an n by n (unit, or non-unit, upper or lower triangular matrix)

The vectors x_i are overwritten.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of matrices A_i. m >= 0.
AP – [in] device pointer storing pointer of matrices A_i, of dimension ( lda, m )
lda – [in] [int] specifies the leading dimension of A_i. lda >= max( 1, m ).
x – [in] device pointer storing vectors x_i.
incx – [in] [int] specifies the increment for the elements of vectors x_i.
batchCount – [in] [int] The number of batched matrices/vectors.

hipblasStatus_t hipblasStrmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, int lda, hipblasStride strideA, float *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasDtrmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, int lda, hipblasStride strideA, double *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCtrmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, int lda, hipblasStride strideA, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZtrmvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

BLAS Level 2 API.

trmvStridedBatched performs one of the matrix-vector operations

 x_i = A_i*x_i or x_i = A**T*x_i, 0 \le i < batchCount

where x_i is an n element vector and A_i is an n by n (unit, or non-unit, upper or lower triangular matrix) with strides specifying how to retrieve $x_i$ (resp. $A_i$) from $x_{i-1}$ (resp. $A_i$).

The vectors x_i are overwritten.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of matrices A_i. m >= 0.
AP – [in] device pointer of the matrix A_0, of dimension ( lda, m )
lda – [in] [int] specifies the leading dimension of A_i. lda >= max( 1, m ).
strideA – [in] [hipblasStride] stride from the start of one A_i matrix to the next A_{i + 1}
x – [in] device pointer storing the vector x_0.
incx – [in] [int] specifies the increment for the elements of one vector x.
stridex – [in] [hipblasStride] stride from the start of one x_i vector to the next x_{i + 1}
batchCount – [in] [int] The number of batched matrices/vectors.

hipblasXtrsv + Batched, StridedBatched #

hipblasStatus_t hipblasStrsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, int lda, float *x, int incx)#

hipblasStatus_t hipblasDtrsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, int lda, double *x, int incx)#

hipblasStatus_t hipblasCtrsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, int lda, hipblasComplex *x, int incx)#

hipblasStatus_t hipblasZtrsv(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, int lda, hipblasDoubleComplex *x, int incx)#

BLAS Level 2 API.

trsv solves

 A*x = b or A**T*x = b,

where x and b are vectors and A is a triangular matrix.

The vector x is overwritten on b.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of b. m >= 0.
AP – [in] device pointer storing matrix A, of dimension ( lda, m )
lda – [in] [int] specifies the leading dimension of A. lda = max( 1, m ).
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment for the elements of x.

hipblasStatus_t hipblasStrsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *const AP[], int lda, float *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasDtrsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *const AP[], int lda, double *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasCtrsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *const AP[], int lda, hipblasComplex *const x[], int incx, int batchCount)#

hipblasStatus_t hipblasZtrsvBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *const AP[], int lda, hipblasDoubleComplex *const x[], int incx, int batchCount)#

BLAS Level 2 API.

trsvBatched solves

 A_i*x_i = b_i or A_i**T*x_i = b_i,

where (A_i, x_i, b_i) is the i-th instance of the batch. x_i and b_i are vectors and A_i is an m by m triangular matrix.

The vector x is overwritten on b.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of b. m >= 0.
AP – [in] device array of device pointers storing each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i. lda = max(1, m)
x – [in] device array of device pointers storing each vector x_i.
incx – [in] [int] specifies the increment for the elements of x.
batchCount – [in] [int] number of instances in the batch

hipblasStatus_t hipblasStrsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const float *AP, int lda, hipblasStride strideA, float *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasDtrsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const double *AP, int lda, hipblasStride strideA, double *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasCtrsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasComplex *AP, int lda, hipblasStride strideA, hipblasComplex *x, int incx, hipblasStride stridex, int batchCount)#

hipblasStatus_t hipblasZtrsvStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, hipblasDoubleComplex *x, int incx, hipblasStride stridex, int batchCount)#

BLAS Level 2 API.

trsvStridedBatched solves

 A_i*x_i = b_i or A_i**T*x_i = b_i,

where (A_i, x_i, b_i) is the i-th instance of the batch. x_i and b_i are vectors and A_i is an m by m triangular matrix, for i = 1, …, batchCount.

The vector x is overwritten on b.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t]
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of each b_i. m >= 0.
AP – [in] device pointer to the first matrix (A_1) in the batch, of dimension ( lda, m )
strideA – [in] [hipblasStride] stride from the start of one A_i matrix to the next A_(i + 1)
lda – [in] [int] specifies the leading dimension of each A_i. lda = max( 1, m ).
x – [inout] device pointer to the first vector (x_1) in the batch.
stridex – [in] [hipblasStride] stride from the start of one x_i vector to the next x_(i + 1)
incx – [in] [int] specifies the increment for the elements of each x_i.
batchCount – [in] [int] number of instances in the batch

Level 3 BLAS#

hipblasXgemm + Batched, StridedBatched #

hipblasStatus_t hipblasHgemm(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasHalf *alpha, const hipblasHalf *AP, int lda, const hipblasHalf *BP, int ldb, const hipblasHalf *beta, hipblasHalf *CP, int ldc)#

hipblasStatus_t hipblasSgemm(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const float *alpha, const float *AP, int lda, const float *BP, int ldb, const float *beta, float *CP, int ldc)#

hipblasStatus_t hipblasDgemm(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const double *alpha, const double *AP, int lda, const double *BP, int ldb, const double *beta, double *CP, int ldc)#

hipblasStatus_t hipblasCgemm(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *BP, int ldb, const hipblasComplex *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZgemm(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *BP, int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

gemm performs one of the matrix-matrix operations

C = alpha*op( A )*op( B ) + beta*C,

where op( X ) is one of

op( X ) = X      or
op( X ) = X**T   or
op( X ) = X**H,

alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix.

Supported precisions in rocBLAS : h,s,d,c,z
Supported precisions in cuBLAS : h,s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t]
```
      .
```
transA – [in] [hipblasOperation_t] specifies the form of op( A )
transB – [in] [hipblasOperation_t] specifies the form of op( B )
m – [in] [int] number or rows of matrices op( A ) and C
n – [in] [int] number of columns of matrices op( B ) and C
k – [in] [int] number of columns of matrix op( A ) and number of rows of matrix op( B )
alpha – [in] device pointer or host pointer specifying the scalar alpha.
AP – [in] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.
BP – [in] device pointer storing matrix B.
ldb – [in] [int] specifies the leading dimension of B.
beta – [in] device pointer or host pointer specifying the scalar beta.
CP – [inout] device pointer storing matrix C on the GPU.
ldc – [in] [int] specifies the leading dimension of C.

hipblasStatus_t hipblasHgemmBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasHalf *alpha, const hipblasHalf *const AP[], int lda, const hipblasHalf *const BP[], int ldb, const hipblasHalf *beta, hipblasHalf *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasSgemmBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const float *alpha, const float *const AP[], int lda, const float *const BP[], int ldb, const float *beta, float *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasDgemmBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const double *alpha, const double *const AP[], int lda, const double *const BP[], int ldb, const double *beta, double *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasCgemmBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const BP[], int ldb, const hipblasComplex *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZgemmBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const BP[], int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

gemmBatched performs one of the batched matrix-matrix operations C_i = alpha*op( A_i )*op( B_i ) + beta*C_i, for i = 1, …, batchCount. where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are strided batched matrices, with op( A ) an m by k by batchCount strided_batched matrix, op( B ) an k by n by batchCount strided_batched matrix and C an m by n by batchCount strided_batched matrix.

Supported precisions in rocBLAS : h,s,d,c,z
Supported precisions in cuBLAS : h,s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A )
transB – [in] [hipblasOperation_t] specifies the form of op( B )
m – [in] [int] matrix dimention m.
n – [in] [int] matrix dimention n.
k – [in] [int] matrix dimention k.
alpha – [in] device pointer or host pointer specifying the scalar alpha.
AP – [in] device array of device pointers storing each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i.
BP – [in] device array of device pointers storing each matrix B_i.
ldb – [in] [int] specifies the leading dimension of each B_i.
beta – [in] device pointer or host pointer specifying the scalar beta.
CP – [inout] device array of device pointers storing each matrix C_i.
ldc – [in] [int] specifies the leading dimension of each C_i.
batchCount – [in] [int] number of gemm operations in the batch

hipblasStatus_t hipblasHgemmStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasHalf *alpha, const hipblasHalf *AP, int lda, long long strideA, const hipblasHalf *BP, int ldb, long long strideB, const hipblasHalf *beta, hipblasHalf *CP, int ldc, long long strideC, int batchCount)#

hipblasStatus_t hipblasSgemmStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const float *alpha, const float *AP, int lda, long long strideA, const float *BP, int ldb, long long strideB, const float *beta, float *CP, int ldc, long long strideC, int batchCount)#

hipblasStatus_t hipblasDgemmStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const double *alpha, const double *AP, int lda, long long strideA, const double *BP, int ldb, long long strideB, const double *beta, double *CP, int ldc, long long strideC, int batchCount)#

hipblasStatus_t hipblasCgemmStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, long long strideA, const hipblasComplex *BP, int ldb, long long strideB, const hipblasComplex *beta, hipblasComplex *CP, int ldc, long long strideC, int batchCount)#

hipblasStatus_t hipblasZgemmStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, long long strideA, const hipblasDoubleComplex *BP, int ldb, long long strideB, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc, long long strideC, int batchCount)#

BLAS Level 3 API.

gemmStridedBatched performs one of the strided batched matrix-matrix operations

C_i = alpha*op( A_i )*op( B_i ) + beta*C_i, for i = 1, ..., batchCount.

where op( X ) is one of

op( X ) = X      or
op( X ) = X**T   or
op( X ) = X**H,

alpha and beta are scalars, and A, B and C are strided batched matrices, with op( A ) an m by k by batchCount strided_batched matrix, op( B ) an k by n by batchCount strided_batched matrix and C an m by n by batchCount strided_batched matrix.

Supported precisions in rocBLAS : h,s,d,c,z
Supported precisions in cuBLAS : h,s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A )
transB – [in] [hipblasOperation_t] specifies the form of op( B )
m – [in] [int] matrix dimention m.
n – [in] [int] matrix dimention n.
k – [in] [int] matrix dimention k.
alpha – [in] device pointer or host pointer specifying the scalar alpha.
AP – [in] device pointer pointing to the first matrix A_1.
lda – [in] [int] specifies the leading dimension of each A_i.
strideA – [in] [hipblasStride] stride from the start of one A_i matrix to the next A_(i + 1).
BP – [in] device pointer pointing to the first matrix B_1.
ldb – [in] [int] specifies the leading dimension of each B_i.
strideB – [in] [hipblasStride] stride from the start of one B_i matrix to the next B_(i + 1).
beta – [in] device pointer or host pointer specifying the scalar beta.
CP – [inout] device pointer pointing to the first matrix C_1.
ldc – [in] [int] specifies the leading dimension of each C_i.
strideC – [in] [hipblasStride] stride from the start of one C_i matrix to the next C_(i + 1).
batchCount – [in] [int] number of gemm operatons in the batch

hipblasXherk + Batched, StridedBatched #

hipblasStatus_t hipblasCherk(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const hipblasComplex *AP, int lda, const float *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZherk(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const hipblasDoubleComplex *AP, int lda, const double *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

herk performs one of the matrix-matrix operations for a Hermitian rank-k update

C := alpha*op( A )*op( A )^H + beta*C

where alpha and beta are scalars, op(A) is an n by k matrix, and C is a n x n Hermitian matrix stored as either upper or lower.

op( A ) = A,  and A is n by k if transA == HIPBLAS_OP_N
op( A ) = A^H and A is k by n if transA == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op(A) = A^H HIPBLAS_ON_N: op(A) = A
n – [in] [int] n specifies the number of rows and columns of C. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] pointer storing matrix A on the GPU. Martrix dimension is ( lda, k ) when if transA = HIPBLAS_OP_N, otherwise (lda, n) only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if transA = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).

hipblasStatus_t hipblasCherkBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const hipblasComplex *const AP[], int lda, const float *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZherkBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const hipblasDoubleComplex *const AP[], int lda, const double *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

herkBatched performs a batch of the matrix-matrix operations for a Hermitian rank-k update

C_i := alpha*op( A_i )*op( A_i )^H + beta*C_i

where alpha and beta are scalars, op(A) is an n by k matrix, and C_i is a n x n Hermitian matrix stored as either upper or lower.

op( A_i ) = A_i, and A_i is n by k if transA == HIPBLAS_OP_N
op( A_i ) = A_i^H and A_i is k by n if transA == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op(A) = A^H HIPBLAS_OP_N: op(A) = A
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] device array of device pointers storing each matrix_i A of dimension (lda, k) when transA is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if transA = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasCherkStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const float *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZherkStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const double *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

herkStridedBatched performs a batch of the matrix-matrix operations for a Hermitian rank-k update

C_i := alpha*op( A_i )*op( A_i )^H + beta*C_i

where alpha and beta are scalars, op(A) is an n by k matrix, and C_i is a n x n Hermitian matrix stored as either upper or lower.

op( A_i ) = A_i, and A_i is n by k if transA == HIPBLAS_OP_N
op( A_i ) = A_i^H and A_i is k by n if transA == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op(A) = A^H HIPBLAS_OP_N: op(A) = A
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] Device pointer to the first matrix A_1 on the GPU of dimension (lda, k) when transA is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if transA = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] Device pointer to the first matrix C_1 on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXherkx + Batched, StridedBatched #

hipblasStatus_t hipblasCherkx(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *BP, int ldb, const float *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZherkx(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *BP, int ldb, const double *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

herkx performs one of the matrix-matrix operations for a Hermitian rank-k update

C := alpha*op( A )*op( B )^H + beta*C

where alpha and beta are scalars, op(A) and op(B) are n by k matrices, and C is a n x n Hermitian matrix stored as either upper or lower. This routine should only be used when the caller can guarantee that the result of op( A )*op( B )^T will be Hermitian.

op( A ) = A, op( B ) = B, and A and B are n by k if trans == HIPBLAS_OP_N
op( A ) = A^H, op( B ) = B^H,  and A and B are k by n if trans == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op( A ) = A^H, op( B ) = B^H HIPBLAS_OP_N: op( A ) = A, op( B ) = B
n – [in] [int] n specifies the number of rows and columns of C. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] pointer storing matrix A on the GPU. Martrix dimension is ( lda, k ) when if trans = HIPBLAS_OP_N, otherwise (lda, n) only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] pointer storing matrix B on the GPU. Martrix dimension is ( ldb, k ) when if trans = HIPBLAS_OP_N, otherwise (ldb, n) only the upper/lower triangular part is accessed.
ldb – [in] [int] ldb specifies the first dimension of B. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).

hipblasStatus_t hipblasCherkxBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const BP[], int ldb, const float *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZherkxBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const BP[], int ldb, const double *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

herkxBatched performs a batch of the matrix-matrix operations for a Hermitian rank-k update

C_i := alpha*op( A_i )*op( B_i )^H + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrices, and C_i is a n x n Hermitian matrix stored as either upper or lower. This routine should only be used when the caller can guarantee that the result of op( A )*op( B )^T will be Hermitian.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^H, op( B_i ) = B_i^H,  and A_i and B_i are k by n if trans == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op(A) = A^H HIPBLAS_OP_N: op(A) = A
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] device array of device pointers storing each matrix_i A of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] device array of device pointers storing each matrix_i B of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B_i. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasCherkxStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *BP, int ldb, hipblasStride strideB, const float *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZherkxStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, const double *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

herkxStridedBatched performs a batch of the matrix-matrix operations for a Hermitian rank-k update

C_i := alpha*op( A_i )*op( B_i )^H + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrices, and C_i is a n x n Hermitian matrix stored as either upper or lower. This routine should only be used when the caller can guarantee that the result of op( A )*op( B )^T will be Hermitian.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^H, op( B_i ) = B_i^H,  and A_i and B_i are k by n if trans == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op( A_i ) = A_i^H, op( B_i ) = B_i^H HIPBLAS_OP_N: op( A_i ) = A_i, op( B_i ) = B_i
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] Device pointer to the first matrix A_1 on the GPU of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
BP – [in] Device pointer to the first matrix B_1 on the GPU of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B_i. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] Device pointer to the first matrix C_1 on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXher2k + Batched, StridedBatched #

hipblasStatus_t hipblasCher2k(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *BP, int ldb, const float *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZher2k(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *BP, int ldb, const double *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

her2k performs one of the matrix-matrix operations for a Hermitian rank-2k update

C := alpha*op( A )*op( B )^H + conj(alpha)*op( B )*op( A )^H + beta*C

where alpha and beta are scalars, op(A) and op(B) are n by k matrices, and C is a n x n Hermitian matrix stored as either upper or lower.

op( A ) = A, op( B ) = B, and A and B are n by k if trans == HIPBLAS_OP_N
op( A ) = A^H, op( B ) = B^H,  and A and B are k by n if trans == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op( A ) = A^H, op( B ) = B^H HIPBLAS_OP_N: op( A ) = A, op( B ) = B
n – [in] [int] n specifies the number of rows and columns of C. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] pointer storing matrix A on the GPU. Martrix dimension is ( lda, k ) when if trans = HIPBLAS_OP_N, otherwise (lda, n) only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] pointer storing matrix B on the GPU. Martrix dimension is ( ldb, k ) when if trans = HIPBLAS_OP_N, otherwise (ldb, n) only the upper/lower triangular part is accessed.
ldb – [in] [int] ldb specifies the first dimension of B. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).

hipblasStatus_t hipblasCher2kBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const BP[], int ldb, const float *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZher2kBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const BP[], int ldb, const double *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

her2kBatched performs a batch of the matrix-matrix operations for a Hermitian rank-2k update

C_i := alpha*op( A_i )*op( B_i )^H + conj(alpha)*op( B_i )*op( A_i )^H + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrices, and C_i is a n x n Hermitian matrix stored as either upper or lower.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^H, op( B_i ) = B_i^H,  and A_i and B_i are k by n if trans == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op(A) = A^H HIPBLAS_OP_N: op(A) = A
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] device array of device pointers storing each matrix_i A of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] device array of device pointers storing each matrix_i B of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B_i. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasCher2kStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *BP, int ldb, hipblasStride strideB, const float *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZher2kStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, const double *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

her2kStridedBatched performs a batch of the matrix-matrix operations for a Hermitian rank-2k update

C_i := alpha*op( A_i )*op( B_i )^H + conj(alpha)*op( B_i )*op( A_i )^H + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrices, and C_i is a n x n Hermitian matrix stored as either upper or lower.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^H, op( B_i ) = B_i^H,  and A_i and B_i are k by n if trans == HIPBLAS_OP_C

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_C: op( A_i ) = A_i^H, op( B_i ) = B_i^H HIPBLAS_OP_N: op( A_i ) = A_i, op( B_i ) = B_i
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] Device pointer to the first matrix A_1 on the GPU of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
BP – [in] Device pointer to the first matrix B_1 on the GPU of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B_i. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] Device pointer to the first matrix C_1 on the GPU. The imaginary component of the diagonal elements are not used but are set to zero unless quick return.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXsymm + Batched, StridedBatched #

hipblasStatus_t hipblasSsymm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const float *alpha, const float *AP, int lda, const float *BP, int ldb, const float *beta, float *CP, int ldc)#

hipblasStatus_t hipblasDsymm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const double *alpha, const double *AP, int lda, const double *BP, int ldb, const double *beta, double *CP, int ldc)#

hipblasStatus_t hipblasCsymm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *BP, int ldb, const hipblasComplex *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZsymm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *BP, int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

symm performs one of the matrix-matrix operations:

C := alpha*A*B + beta*C if side == HIPBLAS_SIDE_LEFT, C := alpha*B*A + beta*C if side == HIPBLAS_SIDE_RIGHT,

where alpha and beta are scalars, B and C are m by n matrices, and A is a symmetric matrix stored as either upper or lower.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: C := alpha*A*B + beta*C HIPBLAS_SIDE_RIGHT: C := alpha*B*A + beta*C
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix
m – [in] [int] m specifies the number of rows of B and C. m >= 0.
n – [in] [int] n specifies the number of columns of B and C. n >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A and B are not referenced.
AP – [in] pointer storing matrix A on the GPU. A is m by m if side == HIPBLAS_SIDE_LEFT A is n by n if side == HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), otherwise lda >= max( 1, n ).
BP – [in] pointer storing matrix B on the GPU. Matrix dimension is m by n
ldb – [in] [int] ldb specifies the first dimension of B. ldb >= max( 1, m )
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU. Matrix dimension is m by n
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, m )

hipblasStatus_t hipblasSsymmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const float *alpha, const float *const AP[], int lda, const float *const BP[], int ldb, const float *beta, float *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasDsymmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const double *alpha, const double *const AP[], int lda, const double *const BP[], int ldb, const double *beta, double *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasCsymmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const BP[], int ldb, const hipblasComplex *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZsymmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const BP[], int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

symmBatched performs a batch of the matrix-matrix operations:

C_i := alpha*A_i*B_i + beta*C_i if side == HIPBLAS_SIDE_LEFT, C_i := alpha*B_i*A_i + beta*C_i if side == HIPBLAS_SIDE_RIGHT,

where alpha and beta are scalars, B_i and C_i are m by n matrices, and A_i is a symmetric matrix stored as either upper or lower.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: C_i := alpha*A_i*B_i + beta*C_i HIPBLAS_SIDE_RIGHT: C_i := alpha*B_i*A_i + beta*C_i
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix
m – [in] [int] m specifies the number of rows of B_i and C_i. m >= 0.
n – [in] [int] n specifies the number of columns of B_i and C_i. n >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A_i and B_i are not referenced.
AP – [in] device array of device pointers storing each matrix A_i on the GPU. A_i is m by m if side == HIPBLAS_SIDE_LEFT A_i is n by n if side == HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A_i. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), otherwise lda >= max( 1, n ).
BP – [in] device array of device pointers storing each matrix B_i on the GPU. Matrix dimension is m by n
ldb – [in] [int] ldb specifies the first dimension of B_i. ldb >= max( 1, m )
beta – [in] beta specifies the scalar beta. When beta is zero then C_i need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU. Matrix dimension is m by n
ldc – [in] [int] ldc specifies the first dimension of C_i. ldc >= max( 1, m )
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSsymmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *BP, int ldb, hipblasStride strideB, const float *beta, float *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasDsymmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *BP, int ldb, hipblasStride strideB, const double *beta, double *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasCsymmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *BP, int ldb, hipblasStride strideB, const hipblasComplex *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZsymmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

symmStridedBatched performs a batch of the matrix-matrix operations:

C_i := alpha*A_i*B_i + beta*C_i if side == HIPBLAS_SIDE_LEFT, C_i := alpha*B_i*A_i + beta*C_i if side == HIPBLAS_SIDE_RIGHT,

where alpha and beta are scalars, B_i and C_i are m by n matrices, and A_i is a symmetric matrix stored as either upper or lower.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: C_i := alpha*A_i*B_i + beta*C_i HIPBLAS_SIDE_RIGHT: C_i := alpha*B_i*A_i + beta*C_i
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix
m – [in] [int] m specifies the number of rows of B_i and C_i. m >= 0.
n – [in] [int] n specifies the number of columns of B_i and C_i. n >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A_i and B_i are not referenced.
AP – [in] device pointer to first matrix A_1 A_i is m by m if side == HIPBLAS_SIDE_LEFT A_i is n by n if side == HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A_i. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), otherwise lda >= max( 1, n ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
BP – [in] device pointer to first matrix B_1 of dimension (ldb, n) on the GPU.
ldb – [in] [int] ldb specifies the first dimension of B_i. ldb >= max( 1, m )
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device pointer to first matrix C_1 of dimension (ldc, n) on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, m ).
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXsyrk + Batched, StridedBatched #

hipblasStatus_t hipblasSsyrk(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *AP, int lda, const float *beta, float *CP, int ldc)#

hipblasStatus_t hipblasDsyrk(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *AP, int lda, const double *beta, double *CP, int ldc)#

hipblasStatus_t hipblasCsyrk(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZsyrk(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

syrk performs one of the matrix-matrix operations for a symmetric rank-k update

C := alpha*op( A )*op( A )^T + beta*C

where alpha and beta are scalars, op(A) is an n by k matrix, and C is a symmetric n x n matrix stored as either upper or lower.

op( A ) = A, and A is n by k if transA == HIPBLAS_OP_N
op( A ) = A^T and A is k by n if transA == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

HIPBLAS_OP_C is not supported for complex types, see cherk and zherk.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op(A) = A^T HIPBLAS_OP_N: op(A) = A HIPBLAS_OP_C: op(A) = A^T
n – [in] [int] n specifies the number of rows and columns of C. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] pointer storing matrix A on the GPU. Martrix dimension is ( lda, k ) when if transA = HIPBLAS_OP_N, otherwise (lda, n) only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if transA = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).

hipblasStatus_t hipblasSsyrkBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *const AP[], int lda, const float *beta, float *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasDsyrkBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *const AP[], int lda, const double *beta, double *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasCsyrkBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZsyrkBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

syrkBatched performs a batch of the matrix-matrix operations for a symmetric rank-k update

C_i := alpha*op( A_i )*op( A_i )^T + beta*C_i

where alpha and beta are scalars, op(A_i) is an n by k matrix, and C_i is a symmetric n x n matrix stored as either upper or lower.

op( A_i ) = A_i, and A_i is n by k if transA == HIPBLAS_OP_N
op( A_i ) = A_i^T and A_i is k by n if transA == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

HIPBLAS_OP_C is not supported for complex types, see cherk and zherk.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op(A) = A^T HIPBLAS_OP_N: op(A) = A HIPBLAS_OP_C: op(A) = A^T
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] device array of device pointers storing each matrix_i A of dimension (lda, k) when transA is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if transA = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSsyrkStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *beta, float *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasDsyrkStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *beta, double *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasCsyrkStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZsyrkStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

syrkStridedBatched performs a batch of the matrix-matrix operations for a symmetric rank-k update

C_i := alpha*op( A_i )*op( A_i )^T + beta*C_i

where alpha and beta are scalars, op(A_i) is an n by k matrix, and C_i is a symmetric n x n matrix stored as either upper or lower.

op( A_i ) = A_i, and A_i is n by k if transA == HIPBLAS_OP_N
op( A_i ) = A_i^T and A_i is k by n if transA == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

HIPBLAS_OP_C is not supported for complex types, see cherk and zherk.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op(A) = A^T HIPBLAS_OP_N: op(A) = A HIPBLAS_OP_C: op(A) = A^T
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] Device pointer to the first matrix A_1 on the GPU of dimension (lda, k) when transA is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if transA = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] Device pointer to the first matrix C_1 on the GPU. on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXsyr2k + Batched, StridedBatched #

hipblasStatus_t hipblasSsyr2k(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *AP, int lda, const float *BP, int ldb, const float *beta, float *CP, int ldc)#

hipblasStatus_t hipblasDsyr2k(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *AP, int lda, const double *BP, int ldb, const double *beta, double *CP, int ldc)#

hipblasStatus_t hipblasCsyr2k(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *BP, int ldb, const hipblasComplex *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZsyr2k(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *BP, int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

syr2k performs one of the matrix-matrix operations for a symmetric rank-2k update

C := alpha*(op( A )*op( B )^T + op( B )*op( A )^T) + beta*C

where alpha and beta are scalars, op(A) and op(B) are n by k matrix, and C is a symmetric n x n matrix stored as either upper or lower.

op( A ) = A, op( B ) = B, and A and B are n by k if trans == HIPBLAS_OP_N
op( A ) = A^T, op( B ) = B^T,  and A and B are k by n if trans == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op( A ) = A^T, op( B ) = B^T HIPBLAS_OP_N: op( A ) = A, op( B ) = B
n – [in] [int] n specifies the number of rows and columns of C. n >= 0.
k – [in] [int] k specifies the number of columns of op(A) and op(B). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] pointer storing matrix A on the GPU. Martrix dimension is ( lda, k ) when if trans = HIPBLAS_OP_N, otherwise (lda, n) only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] pointer storing matrix B on the GPU. Martrix dimension is ( ldb, k ) when if trans = HIPBLAS_OP_N, otherwise (ldb, n) only the upper/lower triangular part is accessed.
ldb – [in] [int] ldb specifies the first dimension of B. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).

hipblasStatus_t hipblasSsyr2kBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *const AP[], int lda, const float *const BP[], int ldb, const float *beta, float *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasDsyr2kBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *const AP[], int lda, const double *const BP[], int ldb, const double *beta, double *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasCsyr2kBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const BP[], int ldb, const hipblasComplex *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZsyr2kBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const BP[], int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

syr2kBatched performs a batch of the matrix-matrix operations for a symmetric rank-2k update

C_i := alpha*(op( A_i )*op( B_i )^T + op( B_i )*op( A_i )^T) + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrix, and C_i is a symmetric n x n matrix stored as either upper or lower.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^T, op( B_i ) = B_i^T,  and A_i and B_i are k by n if trans == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op( A_i ) = A_i^T, op( B_i ) = B_i^T HIPBLAS_OP_N: op( A_i ) = A_i, op( B_i ) = B_i
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] device array of device pointers storing each matrix_i A of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] device array of device pointers storing each matrix_i B of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSsyr2kStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *BP, int ldb, hipblasStride strideB, const float *beta, float *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasDsyr2kStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *BP, int ldb, hipblasStride strideB, const double *beta, double *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasCsyr2kStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *BP, int ldb, hipblasStride strideB, const hipblasComplex *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZsyr2kStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

syr2kStridedBatched performs a batch of the matrix-matrix operations for a symmetric rank-2k update

C_i := alpha*(op( A_i )*op( B_i )^T + op( B_i )*op( A_i )^T) + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrix, and C_i is a symmetric n x n matrix stored as either upper or lower.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^T, op( B_i ) = B_i^T,  and A_i and B_i are k by n if trans == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op( A_i ) = A_i^T, op( B_i ) = B_i^T HIPBLAS_OP_N: op( A_i ) = A_i, op( B_i ) = B_i
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] Device pointer to the first matrix A_1 on the GPU of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
BP – [in] Device pointer to the first matrix B_1 on the GPU of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B_i. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] Device pointer to the first matrix C_1 on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXsyrkx + Batched, StridedBatched #

hipblasStatus_t hipblasSsyrkx(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *AP, int lda, const float *BP, int ldb, const float *beta, float *CP, int ldc)#

hipblasStatus_t hipblasDsyrkx(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *AP, int lda, const double *BP, int ldb, const double *beta, double *CP, int ldc)#

hipblasStatus_t hipblasCsyrkx(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *BP, int ldb, const hipblasComplex *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZsyrkx(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *BP, int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

syrkx performs one of the matrix-matrix operations for a symmetric rank-k update

C := alpha*op( A )*op( B )^T + beta*C

where alpha and beta are scalars, op(A) and op(B) are n by k matrix, and C is a symmetric n x n matrix stored as either upper or lower. This routine should only be used when the caller can guarantee that the result of op( A )*op( B )^T will be symmetric.

op( A ) = A, op( B ) = B, and A and B are n by k if trans == HIPBLAS_OP_N
op( A ) = A^T, op( B ) = B^T,  and A and B are k by n if trans == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op( A ) = A^T, op( B ) = B^T HIPBLAS_OP_N: op( A ) = A, op( B ) = B
n – [in] [int] n specifies the number of rows and columns of C. n >= 0.
k – [in] [int] k specifies the number of columns of op(A) and op(B). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] pointer storing matrix A on the GPU. Martrix dimension is ( lda, k ) when if trans = HIPBLAS_OP_N, otherwise (lda, n) only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] pointer storing matrix B on the GPU. Martrix dimension is ( ldb, k ) when if trans = HIPBLAS_OP_N, otherwise (ldb, n) only the upper/lower triangular part is accessed.
ldb – [in] [int] ldb specifies the first dimension of B. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).

hipblasStatus_t hipblasSsyrkxBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *const AP[], int lda, const float *const BP[], int ldb, const float *beta, float *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasDsyrkxBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *const AP[], int lda, const double *const BP[], int ldb, const double *beta, double *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasCsyrkxBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const BP[], int ldb, const hipblasComplex *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZsyrkxBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const BP[], int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

syrkxBatched performs a batch of the matrix-matrix operations for a symmetric rank-k update

C_i := alpha*op( A_i )*op( B_i )^T + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrix, and C_i is a symmetric n x n matrix stored as either upper or lower. This routine should only be used when the caller can guarantee that the result of op( A_i )*op( B_i )^T will be symmetric.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^T, op( B_i ) = B_i^T,  and A_i and B_i are k by n if trans == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op( A_i ) = A_i^T, op( B_i ) = B_i^T HIPBLAS_OP_N: op( A_i ) = A_i, op( B_i ) = B_i
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] device array of device pointers storing each matrix_i A of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
BP – [in] device array of device pointers storing each matrix_i B of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSsyrkxStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *BP, int ldb, hipblasStride strideB, const float *beta, float *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasDsyrkxStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *BP, int ldb, hipblasStride strideB, const double *beta, double *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasCsyrkxStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *BP, int ldb, hipblasStride strideB, const hipblasComplex *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZsyrkxStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasOperation_t transA, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

syrkxStridedBatched performs a batch of the matrix-matrix operations for a symmetric rank-k update

C_i := alpha*op( A_i )*op( B_i )^T + beta*C_i

where alpha and beta are scalars, op(A_i) and op(B_i) are n by k matrix, and C_i is a symmetric n x n matrix stored as either upper or lower. This routine should only be used when the caller can guarantee that the result of op( A_i )*op( B_i )^T will be symmetric.

op( A_i ) = A_i, op( B_i ) = B_i, and A_i and B_i are n by k if trans == HIPBLAS_OP_N
op( A_i ) = A_i^T, op( B_i ) = B_i^T,  and A_i and B_i are k by n if trans == HIPBLAS_OP_T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: C_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: C_i is a lower triangular matrix
transA – [in] [hipblasOperation_t] HIPBLAS_OP_T: op( A_i ) = A_i^T, op( B_i ) = B_i^T HIPBLAS_OP_N: op( A_i ) = A_i, op( B_i ) = B_i
n – [in] [int] n specifies the number of rows and columns of C_i. n >= 0.
k – [in] [int] k specifies the number of columns of op(A). k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and A need not be set before entry.
AP – [in] Device pointer to the first matrix A_1 on the GPU of dimension (lda, k) when trans is HIPBLAS_OP_N, otherwise of dimension (lda, n)
lda – [in] [int] lda specifies the first dimension of A_i. if trans = HIPBLAS_OP_N, lda >= max( 1, n ), otherwise lda >= max( 1, k ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
BP – [in] Device pointer to the first matrix B_1 on the GPU of dimension (ldb, k) when trans is HIPBLAS_OP_N, otherwise of dimension (ldb, n)
ldb – [in] [int] ldb specifies the first dimension of B_i. if trans = HIPBLAS_OP_N, ldb >= max( 1, n ), otherwise ldb >= max( 1, k ).
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] Device pointer to the first matrix C_1 on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, n ).
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch.

hipblasXgeam + Batched, StridedBatched #

hipblasStatus_t hipblasSgeam(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const float *alpha, const float *AP, int lda, const float *beta, const float *BP, int ldb, float *CP, int ldc)#

hipblasStatus_t hipblasDgeam(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const double *alpha, const double *AP, int lda, const double *beta, const double *BP, int ldb, double *CP, int ldc)#

hipblasStatus_t hipblasCgeam(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *beta, const hipblasComplex *BP, int ldb, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZgeam(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *beta, const hipblasDoubleComplex *BP, int ldb, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

geam performs one of the matrix-matrix operations

C = alpha*op( A ) + beta*op( B ),

where op( X ) is one of

op( X ) = X      or
op( X ) = X**T   or
op( X ) = X**H,

alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by n matrix, op( B ) an m by n matrix, and C an m by n matrix.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A )
transB – [in] [hipblasOperation_t] specifies the form of op( B )
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
alpha – [in] device pointer or host pointer specifying the scalar alpha.
AP – [in] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.
beta – [in] device pointer or host pointer specifying the scalar beta.
BP – [in] device pointer storing matrix B.
ldb – [in] [int] specifies the leading dimension of B.
CP – [inout] device pointer storing matrix C.
ldc – [in] [int] specifies the leading dimension of C.

hipblasStatus_t hipblasSgeamBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const float *alpha, const float *const AP[], int lda, const float *beta, const float *const BP[], int ldb, float *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasDgeamBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const double *alpha, const double *const AP[], int lda, const double *beta, const double *const BP[], int ldb, double *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasCgeamBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *beta, const hipblasComplex *const BP[], int ldb, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZgeamBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *beta, const hipblasDoubleComplex *const BP[], int ldb, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

geamBatched performs one of the batched matrix-matrix operations

C_i = alpha*op( A_i ) + beta*op( B_i )  for i = 0, 1, ... batchCount - 1

where alpha and beta are scalars, and op(A_i), op(B_i) and C_i are m by n matrices and op( X ) is one of

op( X ) = X      or
op( X ) = X**T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A )
transB – [in] [hipblasOperation_t] specifies the form of op( B )
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
alpha – [in] device pointer or host pointer specifying the scalar alpha.
AP – [in] device array of device pointers storing each matrix A_i on the GPU. Each A_i is of dimension ( lda, k ), where k is m when transA == HIPBLAS_OP_N and is n when transA == HIPBLAS_OP_T.
lda – [in] [int] specifies the leading dimension of A.
beta – [in] device pointer or host pointer specifying the scalar beta.
BP – [in] device array of device pointers storing each matrix B_i on the GPU. Each B_i is of dimension ( ldb, k ), where k is m when transB == HIPBLAS_OP_N and is n when transB == HIPBLAS_OP_T.
ldb – [in] [int] specifies the leading dimension of B.
CP – [inout] device array of device pointers storing each matrix C_i on the GPU. Each C_i is of dimension ( ldc, n ).
ldc – [in] [int] specifies the leading dimension of C.
batchCount – [in] [int] number of instances i in the batch.

hipblasStatus_t hipblasSgeamStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const float *alpha, const float *AP, int lda, hipblasStride strideA, const float *beta, const float *BP, int ldb, hipblasStride strideB, float *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasDgeamStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const double *alpha, const double *AP, int lda, hipblasStride strideA, const double *beta, const double *BP, int ldb, hipblasStride strideB, double *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasCgeamStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *beta, const hipblasComplex *BP, int ldb, hipblasStride strideB, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZgeamStridedBatched(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *beta, const hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

geamStridedBatched performs one of the batched matrix-matrix operations

C_i = alpha*op( A_i ) + beta*op( B_i )  for i = 0, 1, ... batchCount - 1

where alpha and beta are scalars, and op(A_i), op(B_i) and C_i are m by n matrices and op( X ) is one of

op( X ) = X      or
op( X ) = X**T

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A )
transB – [in] [hipblasOperation_t] specifies the form of op( B )
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
alpha – [in] device pointer or host pointer specifying the scalar alpha.
AP – [in] device pointer to the first matrix A_0 on the GPU. Each A_i is of dimension ( lda, k ), where k is m when transA == HIPBLAS_OP_N and is n when transA == HIPBLAS_OP_T.
lda – [in] [int] specifies the leading dimension of A.
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
beta – [in] device pointer or host pointer specifying the scalar beta.
BP – [in] pointer to the first matrix B_0 on the GPU. Each B_i is of dimension ( ldb, k ), where k is m when transB == HIPBLAS_OP_N and is n when transB == HIPBLAS_OP_T.
ldb – [in] [int] specifies the leading dimension of B.
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
CP – [inout] pointer to the first matrix C_0 on the GPU. Each C_i is of dimension ( ldc, n ).
ldc – [in] [int] specifies the leading dimension of C.
strideC – [in] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances i in the batch.

hipblasXhemm + Batched, StridedBatched #

hipblasStatus_t hipblasChemm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, const hipblasComplex *BP, int ldb, const hipblasComplex *beta, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZhemm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *BP, int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

hemm performs one of the matrix-matrix operations:

C := alpha*A*B + beta*C if side == HIPBLAS_SIDE_LEFT, C := alpha*B*A + beta*C if side == HIPBLAS_SIDE_RIGHT,

where alpha and beta are scalars, B and C are m by n matrices, and A is a Hermitian matrix stored as either upper or lower.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: C := alpha*A*B + beta*C HIPBLAS_SIDE_RIGHT: C := alpha*B*A + beta*C
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix
n – [in] [int] n specifies the number of rows of B and C. n >= 0.
k – [in] [int] n specifies the number of columns of B and C. k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A and B are not referenced.
AP – [in] pointer storing matrix A on the GPU. A is m by m if side == HIPBLAS_SIDE_LEFT A is n by n if side == HIPBLAS_SIDE_RIGHT Only the upper/lower triangular part is accessed. The imaginary component of the diagonal elements is not used.
lda – [in] [int] lda specifies the first dimension of A. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), otherwise lda >= max( 1, n ).
BP – [in] pointer storing matrix B on the GPU. Matrix dimension is m by n
ldb – [in] [int] ldb specifies the first dimension of B. ldb >= max( 1, m )
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] pointer storing matrix C on the GPU. Matrix dimension is m by n
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, m )

hipblasStatus_t hipblasChemmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int n, int k, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, const hipblasComplex *const BP[], int ldb, const hipblasComplex *beta, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZhemmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const BP[], int ldb, const hipblasDoubleComplex *beta, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

hemmBatched performs a batch of the matrix-matrix operations:

C_i := alpha*A_i*B_i + beta*C_i if side == HIPBLAS_SIDE_LEFT, C_i := alpha*B_i*A_i + beta*C_i if side == HIPBLAS_SIDE_RIGHT,

where alpha and beta are scalars, B_i and C_i are m by n matrices, and A_i is a Hermitian matrix stored as either upper or lower.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: C_i := alpha*A_i*B_i + beta*C_i HIPBLAS_SIDE_RIGHT: C_i := alpha*B_i*A_i + beta*C_i
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix
n – [in] [int] n specifies the number of rows of B_i and C_i. n >= 0.
k – [in] [int] k specifies the number of columns of B_i and C_i. k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A_i and B_i are not referenced.
AP – [in] device array of device pointers storing each matrix A_i on the GPU. A_i is m by m if side == HIPBLAS_SIDE_LEFT A_i is n by n if side == HIPBLAS_SIDE_RIGHT Only the upper/lower triangular part is accessed. The imaginary component of the diagonal elements is not used.
lda – [in] [int] lda specifies the first dimension of A_i. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), otherwise lda >= max( 1, n ).
BP – [in] device array of device pointers storing each matrix B_i on the GPU. Matrix dimension is m by n
ldb – [in] [int] ldb specifies the first dimension of B_i. ldb >= max( 1, m )
beta – [in] beta specifies the scalar beta. When beta is zero then C_i need not be set before entry.
CP – [in] device array of device pointers storing each matrix C_i on the GPU. Matrix dimension is m by n
ldc – [in] [int] ldc specifies the first dimension of C_i. ldc >= max( 1, m )
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasChemmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int n, int k, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *BP, int ldb, hipblasStride strideB, const hipblasComplex *beta, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZhemmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, int n, int k, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, const hipblasDoubleComplex *beta, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

hemmStridedBatched performs a batch of the matrix-matrix operations:

C_i := alpha*A_i*B_i + beta*C_i if side == HIPBLAS_SIDE_LEFT, C_i := alpha*B_i*A_i + beta*C_i if side == HIPBLAS_SIDE_RIGHT,

where alpha and beta are scalars, B_i and C_i are m by n matrices, and A_i is a Hermitian matrix stored as either upper or lower.

Supported precisions in rocBLAS : c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: C_i := alpha*A_i*B_i + beta*C_i HIPBLAS_SIDE_RIGHT: C_i := alpha*B_i*A_i + beta*C_i
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A_i is an upper triangular matrix HIPBLAS_FILL_MODE_LOWER: A_i is a lower triangular matrix
n – [in] [int] n specifies the number of rows of B_i and C_i. n >= 0.
k – [in] [int] k specifies the number of columns of B_i and C_i. k >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A_i and B_i are not referenced.
AP – [in] device pointer to first matrix A_1 A_i is m by m if side == HIPBLAS_SIDE_LEFT A_i is n by n if side == HIPBLAS_SIDE_RIGHT Only the upper/lower triangular part is accessed. The imaginary component of the diagonal elements is not used.
lda – [in] [int] lda specifies the first dimension of A_i. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), otherwise lda >= max( 1, n ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
BP – [in] device pointer to first matrix B_1 of dimension (ldb, n) on the GPU
ldb – [in] [int] ldb specifies the first dimension of B_i. if side = HIPBLAS_OP_N, ldb >= max( 1, m ), otherwise ldb >= max( 1, n ).
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
beta – [in] beta specifies the scalar beta. When beta is zero then C need not be set before entry.
CP – [in] device pointer to first matrix C_1 of dimension (ldc, n) on the GPU.
ldc – [in] [int] ldc specifies the first dimension of C. ldc >= max( 1, m )
strideC – [inout] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances in the batch

hipblasXtrmm + Batched, StridedBatched #

hipblasStatus_t hipblasStrmm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const float *alpha, const float *AP, int lda, float *BP, int ldb)#

hipblasStatus_t hipblasDtrmm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const double *alpha, const double *AP, int lda, double *BP, int ldb)#

hipblasStatus_t hipblasCtrmm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasComplex *BP, int ldb)#

hipblasStatus_t hipblasZtrmm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasDoubleComplex *BP, int ldb)#

BLAS Level 3 API.

trmm performs one of the matrix-matrix operations

B := alpha*op( A )*B, or B := alpha*B*op( A )

where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of

op( A ) = A   or   op( A ) = A^T   or   op( A ) = A^H.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

When uplo == HIPBLAS_FILL_MODE_UPPER the leading k by k upper triangular part of the array A must contain the upper triangular matrix and the strictly lower triangular part of A is not referenced.

When uplo == HIPBLAS_FILL_MODE_LOWER the leading k by k lower triangular part of the array A must contain the lower triangular matrix and the strictly upper triangular part of A is not referenced.

Note that when diag == HIPBLAS_DIAG_UNIT the diagonal elements of A are not referenced either, but are assumed to be unity.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] Specifies whether op(A) multiplies B from the left or right as follows: HIPBLAS_SIDE_LEFT: B := alpha*op( A )*B. HIPBLAS_SIDE_RIGHT: B := alpha*B*op( A ).
uplo – [in] [hipblasFillMode_t] Specifies whether the matrix A is an upper or lower triangular matrix as follows: HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t] Specifies the form of op(A) to be used in the matrix multiplication as follows: HIPBLAS_OP_N: op(A) = A. HIPBLAS_OP_T: op(A) = A^T. HIPBLAS_OP_C: op(A) = A^H.
diag – [in] [hipblasDiagType_t] Specifies whether or not A is unit triangular as follows: HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of B. m >= 0.
n – [in] [int] n specifies the number of columns of B. n >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A is not referenced and B need not be set before entry.
AP – [in] Device pointer to matrix A on the GPU. A has dimension ( lda, k ), where k is m when side == HIPBLAS_SIDE_LEFT and is n when side == HIPBLAS_SIDE_RIGHT.
lda – [in] [int] lda specifies the first dimension of A. if side == HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side == HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
BP – [inout] Device pointer to the first matrix B_0 on the GPU. On entry, the leading m by n part of the array B must contain the matrix B, and on exit is overwritten by the transformed matrix.
ldb – [in] [int] ldb specifies the first dimension of B. ldb >= max( 1, m ).

hipblasStatus_t hipblasStrmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const float *alpha, const float *const AP[], int lda, float *const BP[], int ldb, int batchCount)#

hipblasStatus_t hipblasDtrmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const double *alpha, const double *const AP[], int lda, double *const BP[], int ldb, int batchCount)#

hipblasStatus_t hipblasCtrmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasComplex *alpha, const hipblasComplex *const AP[], int lda, hipblasComplex *const BP[], int ldb, int batchCount)#

hipblasStatus_t hipblasZtrmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *const AP[], int lda, hipblasDoubleComplex *const BP[], int ldb, int batchCount)#

BLAS Level 3 API.

trmmBatched performs one of the batched matrix-matrix operations

B_i := alpha*op( A_i )*B_i, or B_i := alpha*B_i*op( A_i ) for i = 0, 1, … batchCount -1

where alpha is a scalar, B_i is an m by n matrix, A_i is a unit, or non-unit, upper or lower triangular matrix and op( A_i ) is one of

op( A_i ) = A_i   or   op( A_i ) = A_i^T   or   op( A_i ) = A_i^H.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

When uplo == HIPBLAS_FILL_MODE_UPPER the leading k by k upper triangular part of the array A must contain the upper triangular matrix and the strictly lower triangular part of A is not referenced.

When uplo == HIPBLAS_FILL_MODE_LOWER the leading k by k lower triangular part of the array A must contain the lower triangular matrix and the strictly upper triangular part of A is not referenced.

Note that when diag == HIPBLAS_DIAG_UNIT the diagonal elements of A_i are not referenced either, but are assumed to be unity.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] Specifies whether op(A_i) multiplies B_i from the left or right as follows: HIPBLAS_SIDE_LEFT: B_i := alpha*op( A_i )*B_i. HIPBLAS_SIDE_RIGHT: B_i := alpha*B_i*op( A_i ).
uplo – [in] [hipblasFillMode_t] Specifies whether the matrix A is an upper or lower triangular matrix as follows: HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t] Specifies the form of op(A_i) to be used in the matrix multiplication as follows: HIPBLAS_OP_N: op(A_i) = A_i. HIPBLAS_OP_T: op(A_i) = A_i^T. HIPBLAS_OP_C: op(A_i) = A_i^H.
diag – [in] [hipblasDiagType_t] Specifies whether or not A_i is unit triangular as follows: HIPBLAS_DIAG_UNIT: A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of B_i. m >= 0.
n – [in] [int] n specifies the number of columns of B_i. n >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A_i is not referenced and B_i need not be set before entry.
AP – [in] Device array of device pointers storing each matrix A_i on the GPU. Each A_i is of dimension ( lda, k ), where k is m when side == HIPBLAS_SIDE_LEFT and is n when side == HIPBLAS_SIDE_RIGHT.
lda – [in] [int] lda specifies the first dimension of A. if side == HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side == HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
BP – [inout] device array of device pointers storing each matrix B_i on the GPU. On entry, the leading m by n part of the array B_i must contain the matrix B_i, and on exit is overwritten by the transformed matrix.
ldb – [in] [int] ldb specifies the first dimension of B_i. ldb >= max( 1, m ).
batchCount – [in] [int] number of instances i in the batch.

hipblasStatus_t hipblasStrmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const float *alpha, const float *AP, int lda, hipblasStride strideA, float *BP, int ldb, hipblasStride strideB, int batchCount)#

hipblasStatus_t hipblasDtrmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const double *alpha, const double *AP, int lda, hipblasStride strideA, double *BP, int ldb, hipblasStride strideB, int batchCount)#

hipblasStatus_t hipblasCtrmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasComplex *alpha, const hipblasComplex *AP, int lda, hipblasStride strideA, hipblasComplex *BP, int ldb, hipblasStride strideB, int batchCount)#

hipblasStatus_t hipblasZtrmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasDoubleComplex *alpha, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, int batchCount)#

BLAS Level 3 API.

trmmStridedBatched performs one of the strided_batched matrix-matrix operations

B_i := alpha*op( A_i )*B_i, or B_i := alpha*B_i*op( A_i ) for i = 0, 1, … batchCount -1

where alpha is a scalar, B_i is an m by n matrix, A_i is a unit, or non-unit, upper or lower triangular matrix and op( A_i ) is one of

op( A_i ) = A_i   or   op( A_i ) = A_i^T   or   op( A_i ) = A_i^H.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

When uplo == HIPBLAS_FILL_MODE_UPPER the leading k by k upper triangular part of the array A must contain the upper triangular matrix and the strictly lower triangular part of A is not referenced.

When uplo == HIPBLAS_FILL_MODE_LOWER the leading k by k lower triangular part of the array A must contain the lower triangular matrix and the strictly upper triangular part of A is not referenced.

Note that when diag == HIPBLAS_DIAG_UNIT the diagonal elements of A_i are not referenced either, but are assumed to be unity.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] Specifies whether op(A_i) multiplies B_i from the left or right as follows: HIPBLAS_SIDE_LEFT: B_i := alpha*op( A_i )*B_i. HIPBLAS_SIDE_RIGHT: B_i := alpha*B_i*op( A_i ).
uplo – [in] [hipblasFillMode_t] Specifies whether the matrix A is an upper or lower triangular matrix as follows: HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t] Specifies the form of op(A_i) to be used in the matrix multiplication as follows: HIPBLAS_OP_N: op(A_i) = A_i. HIPBLAS_OP_T: op(A_i) = A_i^T. HIPBLAS_OP_C: op(A_i) = A_i^H.
diag – [in] [hipblasDiagType_t] Specifies whether or not A_i is unit triangular as follows: HIPBLAS_DIAG_UNIT: A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of B_i. m >= 0.
n – [in] [int] n specifies the number of columns of B_i. n >= 0.
alpha – [in] alpha specifies the scalar alpha. When alpha is zero then A_i is not referenced and B_i need not be set before entry.
AP – [in] Device pointer to the first matrix A_0 on the GPU. Each A_i is of dimension ( lda, k ), where k is m when side == HIPBLAS_SIDE_LEFT and is n when side == HIPBLAS_SIDE_RIGHT.
lda – [in] [int] lda specifies the first dimension of A. if side == HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side == HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
BP – [inout] Device pointer to the first matrix B_0 on the GPU. On entry, the leading m by n part of the array B_i must contain the matrix B_i, and on exit is overwritten by the transformed matrix.
ldb – [in] [int] ldb specifies the first dimension of B_i. ldb >= max( 1, m ).
strideB – [in] [hipblasStride] stride from the start of one matrix (B_i) and the next one (B_i+1)
batchCount – [in] [int] number of instances i in the batch.

hipblasXtrsm + Batched, StridedBatched #

hipblasStatus_t hipblasStrsm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const float *alpha, float *AP, int lda, float *BP, int ldb)#

hipblasStatus_t hipblasDtrsm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const double *alpha, double *AP, int lda, double *BP, int ldb)#

hipblasStatus_t hipblasCtrsm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasComplex *alpha, hipblasComplex *AP, int lda, hipblasComplex *BP, int ldb)#

hipblasStatus_t hipblasZtrsm(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasDoubleComplex *alpha, hipblasDoubleComplex *AP, int lda, hipblasDoubleComplex *BP, int ldb)#

BLAS Level 3 API.

trsm solves

op(A)*X = alpha*B or  X*op(A) = alpha*B,

where alpha is a scalar, X and B are m by n matrices, A is triangular matrix and op(A) is one of

op( A ) = A   or   op( A ) = A^T   or   op( A ) = A^H.

The matrix X is overwritten on B.

Note about memory allocation: When trsm is launched with a k evenly divisible by the internal block size of 128, and is no larger than 10 of these blocks, the API takes advantage of utilizing pre-allocated memory found in the handle to increase overall performance. This memory can be managed by using the environment variable WORKBUF_TRSM_B_CHNK. When this variable is not set the device memory used for temporary storage will default to 1 MB and may result in chunking, which in turn may reduce performance. Under these circumstances it is recommended that WORKBUF_TRSM_B_CHNK be set to the desired chunk of right hand sides to be used at a time.

(where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT)

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: op(A)*X = alpha*B. HIPBLAS_SIDE_RIGHT: X*op(A) = alpha*B.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: op(A) = A. HIPBLAS_OP_T: op(A) = A^T. HIPBLAS_OP_C: op(A) = A^H.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of B. m >= 0.
n – [in] [int] n specifies the number of columns of B. n >= 0.
alpha – [in] device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced and B need not be set before entry.
AP – [in] device pointer storing matrix A. of dimension ( lda, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side = HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
BP – [inout] device pointer storing matrix B.
ldb – [in] [int] ldb specifies the first dimension of B. ldb >= max( 1, m ).

hipblasStatus_t hipblasStrsmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const float *alpha, float *const AP[], int lda, float *BP[], int ldb, int batchCount)#

hipblasStatus_t hipblasDtrsmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const double *alpha, double *const AP[], int lda, double *BP[], int ldb, int batchCount)#

hipblasStatus_t hipblasCtrsmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasComplex *alpha, hipblasComplex *const AP[], int lda, hipblasComplex *BP[], int ldb, int batchCount)#

hipblasStatus_t hipblasZtrsmBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasDoubleComplex *alpha, hipblasDoubleComplex *const AP[], int lda, hipblasDoubleComplex *BP[], int ldb, int batchCount)#

BLAS Level 3 API.

trsmBatched performs the following batched operation:

op(A_i)*X_i = alpha*B_i or  X_i*op(A_i) = alpha*B_i, for i = 1, ..., batchCount.

where alpha is a scalar, X and B are batched m by n matrices, A is triangular batched matrix and op(A) is one of

op( A ) = A   or   op( A ) = A^T   or   op( A ) = A^H.

Each matrix X_i is overwritten on B_i for i = 1, …, batchCount.

Note about memory allocation: When trsm is launched with a k evenly divisible by the internal block size of 128, and is no larger than 10 of these blocks, the API takes advantage of utilizing pre-allocated memory found in the handle to increase overall performance. This memory can be managed by using the environment variable WORKBUF_TRSM_B_CHNK. When this variable is not set the device memory used for temporary storage will default to 1 MB and may result in chunking, which in turn may reduce performance. Under these circumstances it is recommended that WORKBUF_TRSM_B_CHNK be set to the desired chunk of right hand sides to be used at a time. (where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT)

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: op(A)*X = alpha*B. HIPBLAS_SIDE_RIGHT: X*op(A) = alpha*B.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: op(A) = A. HIPBLAS_OP_T: op(A) = A^T. HIPBLAS_OP_C: op(A) = A^H.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of each B_i. m >= 0.
n – [in] [int] n specifies the number of columns of each B_i. n >= 0.
alpha – [in] device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced and B need not be set before entry.
AP – [in] device array of device pointers storing each matrix A_i on the GPU. Matricies are of dimension ( lda, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of each A_i. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side = HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
BP – [inout] device array of device pointers storing each matrix B_i on the GPU.
ldb – [in] [int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ).
batchCount – [in] [int] number of trsm operatons in the batch.

hipblasStatus_t hipblasStrsmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const float *alpha, float *AP, int lda, hipblasStride strideA, float *BP, int ldb, hipblasStride strideB, int batchCount)#

hipblasStatus_t hipblasDtrsmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const double *alpha, double *AP, int lda, hipblasStride strideA, double *BP, int ldb, hipblasStride strideB, int batchCount)#

hipblasStatus_t hipblasCtrsmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasComplex *alpha, hipblasComplex *AP, int lda, hipblasStride strideA, hipblasComplex *BP, int ldb, hipblasStride strideB, int batchCount)#

hipblasStatus_t hipblasZtrsmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const hipblasDoubleComplex *alpha, hipblasDoubleComplex *AP, int lda, hipblasStride strideA, hipblasDoubleComplex *BP, int ldb, hipblasStride strideB, int batchCount)#

BLAS Level 3 API.

trsmSridedBatched performs the following strided batched operation:

op(A_i)*X_i = alpha*B_i or  X_i*op(A_i) = alpha*B_i, for i = 1, ..., batchCount.

where alpha is a scalar, X and B are strided batched m by n matrices, A is triangular strided batched matrix and op(A) is one of

op( A ) = A   or   op( A ) = A^T   or   op( A ) = A^H.

Each matrix X_i is overwritten on B_i for i = 1, …, batchCount.

Note about memory allocation: When trsm is launched with a k evenly divisible by the internal block size of 128, and is no larger than 10 of these blocks, the API takes advantage of utilizing pre-allocated memory found in the handle to increase overall performance. This memory can be managed by using the environment variable WORKBUF_TRSM_B_CHNK. When this variable is not set the device memory used for temporary storage will default to 1 MB and may result in chunking, which in turn may reduce performance. Under these circumstances it is recommended that WORKBUF_TRSM_B_CHNK be set to the desired chunk of right hand sides to be used at a time. (where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT)

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: op(A)*X = alpha*B. HIPBLAS_SIDE_RIGHT: X*op(A) = alpha*B.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: op(A) = A. HIPBLAS_OP_T: op(A) = A^T. HIPBLAS_OP_C: op(A) = A^H.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of each B_i. m >= 0.
n – [in] [int] n specifies the number of columns of each B_i. n >= 0.
alpha – [in] device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced and B need not be set before entry.
AP – [in] device pointer pointing to the first matrix A_1. of dimension ( lda, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of each A_i. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side = HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
strideA – [in] [hipblasStride] stride from the start of one A_i matrix to the next A_(i + 1).
BP – [inout] device pointer pointing to the first matrix B_1.
ldb – [in] [int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ).
strideB – [in] [hipblasStride] stride from the start of one B_i matrix to the next B_(i + 1).
batchCount – [in] [int] number of trsm operatons in the batch.

hipblasXtrtri + Batched, StridedBatched #

hipblasStatus_t hipblasStrtri(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const float *AP, int lda, float *invA, int ldinvA)#

hipblasStatus_t hipblasDtrtri(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const double *AP, int lda, double *invA, int ldinvA)#

hipblasStatus_t hipblasCtrtri(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const hipblasComplex *AP, int lda, hipblasComplex *invA, int ldinvA)#

hipblasStatus_t hipblasZtrtri(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const hipblasDoubleComplex *AP, int lda, hipblasDoubleComplex *invA, int ldinvA)#

BLAS Level 3 API.

trtri compute the inverse of a matrix A, namely, invA

and write the result into invA;

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’ if HIPBLAS_FILL_MODE_UPPER, the lower part of A is not referenced if HIPBLAS_FILL_MODE_LOWER, the upper part of A is not referenced
diag – [in] [hipblasDiagType_t] = ‘HIPBLAS_DIAG_NON_UNIT’, A is non-unit triangular; = ‘HIPBLAS_DIAG_UNIT’, A is unit triangular;
n – [in] [int] size of matrix A and invA
AP – [in] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.
invA – [out] device pointer storing matrix invA.
ldinvA – [in] [int] specifies the leading dimension of invA.

hipblasStatus_t hipblasStrtriBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const float *const AP[], int lda, float *invA[], int ldinvA, int batchCount)#

hipblasStatus_t hipblasDtrtriBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const double *const AP[], int lda, double *invA[], int ldinvA, int batchCount)#

hipblasStatus_t hipblasCtrtriBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const hipblasComplex *const AP[], int lda, hipblasComplex *invA[], int ldinvA, int batchCount)#

hipblasStatus_t hipblasZtrtriBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const hipblasDoubleComplex *const AP[], int lda, hipblasDoubleComplex *invA[], int ldinvA, int batchCount)#

BLAS Level 3 API.

trtriBatched compute the inverse of A_i and write into invA_i where A_i and invA_i are the i-th matrices in the batch, for i = 1, …, batchCount.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’
diag – [in] [hipblasDiagType_t] = ‘HIPBLAS_DIAG_NON_UNIT’, A is non-unit triangular; = ‘HIPBLAS_DIAG_UNIT’, A is unit triangular;
n – [in] [int]
AP – [in] device array of device pointers storing each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i.
invA – [out] device array of device pointers storing the inverse of each matrix A_i. Partial inplace operation is supported, see below. If UPLO = ‘U’, the leading N-by-N upper triangular part of the invA will store the inverse of the upper triangular matrix, and the strictly lower triangular part of invA is cleared. If UPLO = ‘L’, the leading N-by-N lower triangular part of the invA will store the inverse of the lower triangular matrix, and the strictly upper triangular part of invA is cleared.
ldinvA – [in] [int] specifies the leading dimension of each invA_i.
batchCount – [in] [int] numbers of matrices in the batch

hipblasStatus_t hipblasStrtriStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const float *AP, int lda, hipblasStride strideA, float *invA, int ldinvA, hipblasStride stride_invA, int batchCount)#

hipblasStatus_t hipblasDtrtriStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const double *AP, int lda, hipblasStride strideA, double *invA, int ldinvA, hipblasStride stride_invA, int batchCount)#

hipblasStatus_t hipblasCtrtriStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const hipblasComplex *AP, int lda, hipblasStride strideA, hipblasComplex *invA, int ldinvA, hipblasStride stride_invA, int batchCount)#

hipblasStatus_t hipblasZtrtriStridedBatched(hipblasHandle_t handle, hipblasFillMode_t uplo, hipblasDiagType_t diag, int n, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, hipblasDoubleComplex *invA, int ldinvA, hipblasStride stride_invA, int batchCount)#

BLAS Level 3 API.

trtriStridedBatched compute the inverse of A_i and write into invA_i where A_i and invA_i are the i-th matrices in the batch, for i = 1, …, batchCount

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
uplo – [in] [hipblasFillMode_t] specifies whether the upper ‘HIPBLAS_FILL_MODE_UPPER’ or lower ‘HIPBLAS_FILL_MODE_LOWER’
diag – [in] [hipblasDiagType_t] = ‘HIPBLAS_DIAG_NON_UNIT’, A is non-unit triangular; = ‘HIPBLAS_DIAG_UNIT’, A is unit triangular;
n – [in] [int]
AP – [in] device pointer pointing to address of first matrix A_1.
lda – [in] [int] specifies the leading dimension of each A.
strideA – [in] [hipblasStride] “batch stride a”: stride from the start of one A_i matrix to the next A_(i + 1).
invA – [out] device pointer storing the inverses of each matrix A_i. Partial inplace operation is supported, see below. If UPLO = ‘U’, the leading N-by-N upper triangular part of the invA will store the inverse of the upper triangular matrix, and the strictly lower triangular part of invA is cleared. If UPLO = ‘L’, the leading N-by-N lower triangular part of the invA will store the inverse of the lower triangular matrix, and the strictly upper triangular part of invA is cleared.
ldinvA – [in] [int] specifies the leading dimension of each invA_i.
stride_invA – [in] [hipblasStride] “batch stride invA”: stride from the start of one invA_i matrix to the next invA_(i + 1).
batchCount – [in] [int] numbers of matrices in the batch

hipblasXdgmm + Batched, StridedBatched #

hipblasStatus_t hipblasSdgmm(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const float *AP, int lda, const float *x, int incx, float *CP, int ldc)#

hipblasStatus_t hipblasDdgmm(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const double *AP, int lda, const double *x, int incx, double *CP, int ldc)#

hipblasStatus_t hipblasCdgmm(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const hipblasComplex *AP, int lda, const hipblasComplex *x, int incx, hipblasComplex *CP, int ldc)#

hipblasStatus_t hipblasZdgmm(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const hipblasDoubleComplex *AP, int lda, const hipblasDoubleComplex *x, int incx, hipblasDoubleComplex *CP, int ldc)#

BLAS Level 3 API.

dgmm performs one of the matrix-matrix operations

C = A * diag(x) if side == HIPBLAS_SIDE_RIGHT
C = diag(x) * A if side == HIPBLAS_SIDE_LEFT

where C and A are m by n dimensional matrices. diag( x ) is a diagonal matrix and x is vector of dimension n if side == HIPBLAS_SIDE_RIGHT and dimension m if side == HIPBLAS_SIDE_LEFT.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] specifies the side of diag(x)
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
AP – [in] device pointer storing matrix A.
lda – [in] [int] specifies the leading dimension of A.
x – [in] device pointer storing vector x.
incx – [in] [int] specifies the increment between values of x
CP – [inout] device pointer storing matrix C.
ldc – [in] [int] specifies the leading dimension of C.

hipblasStatus_t hipblasSdgmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const float *const AP[], int lda, const float *const x[], int incx, float *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasDdgmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const double *const AP[], int lda, const double *const x[], int incx, double *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasCdgmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const hipblasComplex *const AP[], int lda, const hipblasComplex *const x[], int incx, hipblasComplex *const CP[], int ldc, int batchCount)#

hipblasStatus_t hipblasZdgmmBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const hipblasDoubleComplex *const AP[], int lda, const hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const CP[], int ldc, int batchCount)#

BLAS Level 3 API.

dgmmBatched performs one of the batched matrix-matrix operations

C_i = A_i * diag(x_i) for i = 0, 1, ... batchCount-1 if side == HIPBLAS_SIDE_RIGHT
C_i = diag(x_i) * A_i for i = 0, 1, ... batchCount-1 if side == HIPBLAS_SIDE_LEFT

where C_i and A_i are m by n dimensional matrices. diag(x_i) is a diagonal matrix and x_i is vector of dimension n if side == HIPBLAS_SIDE_RIGHT and dimension m if side == HIPBLAS_SIDE_LEFT.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] specifies the side of diag(x)
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
AP – [in] device array of device pointers storing each matrix A_i on the GPU. Each A_i is of dimension ( lda, n )
lda – [in] [int] specifies the leading dimension of A_i.
x – [in] device array of device pointers storing each vector x_i on the GPU. Each x_i is of dimension n if side == HIPBLAS_SIDE_RIGHT and dimension m if side == HIPBLAS_SIDE_LEFT
incx – [in] [int] specifies the increment between values of x_i
CP – [inout] device array of device pointers storing each matrix C_i on the GPU. Each C_i is of dimension ( ldc, n ).
ldc – [in] [int] specifies the leading dimension of C_i.
batchCount – [in] [int] number of instances in the batch.

hipblasStatus_t hipblasSdgmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const float *AP, int lda, hipblasStride strideA, const float *x, int incx, hipblasStride stridex, float *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasDdgmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const double *AP, int lda, hipblasStride strideA, const double *x, int incx, hipblasStride stridex, double *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasCdgmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const hipblasComplex *AP, int lda, hipblasStride strideA, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

hipblasStatus_t hipblasZdgmmStridedBatched(hipblasHandle_t handle, hipblasSideMode_t side, int m, int n, const hipblasDoubleComplex *AP, int lda, hipblasStride strideA, const hipblasDoubleComplex *x, int incx, hipblasStride stridex, hipblasDoubleComplex *CP, int ldc, hipblasStride strideC, int batchCount)#

BLAS Level 3 API.

dgmmStridedBatched performs one of the batched matrix-matrix operations

C_i = A_i * diag(x_i)   if side == HIPBLAS_SIDE_RIGHT   for i = 0, 1, ... batchCount-1
C_i = diag(x_i) * A_i   if side == HIPBLAS_SIDE_LEFT    for i = 0, 1, ... batchCount-1

where C_i and A_i are m by n dimensional matrices. diag(x_i) is a diagonal matrix and x_i is vector of dimension n if side == HIPBLAS_SIDE_RIGHT and dimension m if side == HIPBLAS_SIDE_LEFT.

Supported precisions in rocBLAS : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] specifies the side of diag(x)
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
AP – [in] device pointer to the first matrix A_0 on the GPU. Each A_i is of dimension ( lda, n )
lda – [in] [int] specifies the leading dimension of A.
strideA – [in] [hipblasStride] stride from the start of one matrix (A_i) and the next one (A_i+1)
x – [in] pointer to the first vector x_0 on the GPU. Each x_i is of dimension n if side == HIPBLAS_SIDE_RIGHT and dimension m if side == HIPBLAS_SIDE_LEFT
incx – [in] [int] specifies the increment between values of x
stridex – [in] [hipblasStride] stride from the start of one vector(x_i) and the next one (x_i+1)
CP – [inout] device pointer to the first matrix C_0 on the GPU. Each C_i is of dimension ( ldc, n ).
ldc – [in] [int] specifies the leading dimension of C.
strideC – [in] [hipblasStride] stride from the start of one matrix (C_i) and the next one (C_i+1)
batchCount – [in] [int] number of instances i in the batch.

SOLVER API#

hipblasXgetrf + Batched, stridedBatched #

hipblasStatus_t hipblasSgetrf(hipblasHandle_t handle, const int n, float *A, const int lda, int *ipiv, int *info)#

hipblasStatus_t hipblasDgetrf(hipblasHandle_t handle, const int n, double *A, const int lda, int *ipiv, int *info)#

hipblasStatus_t hipblasCgetrf(hipblasHandle_t handle, const int n, hipblasComplex *A, const int lda, int *ipiv, int *info)#

hipblasStatus_t hipblasZgetrf(hipblasHandle_t handle, const int n, hipblasDoubleComplex *A, const int lda, int *ipiv, int *info)#

SOLVER API.

getrf computes the LU factorization of a general n-by-n matrix A using partial pivoting with row interchanges. The LU factorization can be done without pivoting if ipiv is passed as a nullptr.

In the case that ipiv is not null, the factorization has the form:

\[ A = PLU \]

where P is a permutation matrix, L is lower triangular with unit diagonal elements, and U is upper triangular.

In the case that ipiv is null, the factorization is done without pivoting:

\[ A = LU \]

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
n – [in]
int. n >= 0.

The number of columns and rows of the matrix A.
A – [inout]
pointer to type. Array on the GPU of dimension lda*n.

On entry, the n-by-n matrix A to be factored. On exit, the factors L and U from the factorization. The unit diagonal elements of L are not stored.
lda – [in]
int. lda >= n.

Specifies the leading dimension of A.
ipiv – [out]
pointer to int. Array on the GPU of dimension n.

The vector of pivot indices. Elements of ipiv are 1-based indices. For 1 <= i <= n, the row i of the matrix was interchanged with row ipiv[i]. Matrix P of the factorization can be derived from ipiv. The factorization here can be done without pivoting if ipiv is passed in as a nullptr.
info – [out]
pointer to a int on the GPU.

If info = 0, successful exit. If info = j > 0, U is singular. U[j,j] is the first zero pivot.

hipblasStatus_t hipblasSgetrfBatched(hipblasHandle_t handle, const int n, float *const A[], const int lda, int *ipiv, int *info, const int batchCount)#

hipblasStatus_t hipblasDgetrfBatched(hipblasHandle_t handle, const int n, double *const A[], const int lda, int *ipiv, int *info, const int batchCount)#

hipblasStatus_t hipblasCgetrfBatched(hipblasHandle_t handle, const int n, hipblasComplex *const A[], const int lda, int *ipiv, int *info, const int batchCount)#

hipblasStatus_t hipblasZgetrfBatched(hipblasHandle_t handle, const int n, hipblasDoubleComplex *const A[], const int lda, int *ipiv, int *info, const int batchCount)#

SOLVER API.

getrfBatched computes the LU factorization of a batch of general n-by-n matrices using partial pivoting with row interchanges. The LU factorization can be done without pivoting if ipiv is passed as a nullptr.

In the case that ipiv is not null, the factorization of matrix $A_i$ in the batch has the form:

\[ A_i = P_iL_iU_i \]

where $P_i$ is a permutation matrix, $L_i$ is lower triangular with unit diagonal elements, and $U_i$ is upper triangular.

In the case that ipiv is null, the factorization is done without pivoting:

\[ A_i = L_iU_i \]

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
n – [in]
int. n >= 0.

The number of columns and rows of all matrices A_i in the batch.
A – [inout]
array of pointers to type. Each pointer points to an array on the GPU of dimension lda*n.

On entry, the n-by-n matrices A_i to be factored. On exit, the factors L_i and U_i from the factorizations. The unit diagonal elements of L_i are not stored.
lda – [in]
int. lda >= n.

Specifies the leading dimension of matrices A_i.
ipiv – [out]
pointer to int. Array on the GPU.

Contains the vectors of pivot indices ipiv_i (corresponding to A_i). Dimension of ipiv_i is n. Elements of ipiv_i are 1-based indices. For each instance A_i in the batch and for 1 <= j <= n, the row j of the matrix A_i was interchanged with row ipiv_i[j]. Matrix P_i of the factorization can be derived from ipiv_i. The factorization here can be done without pivoting if ipiv is passed in as a nullptr.
info – [out]
pointer to int. Array of batchCount integers on the GPU.

If info[i] = 0, successful exit for factorization of A_i. If info[i] = j > 0, U_i is singular. U_i[j,j] is the first zero pivot.
batchCount – [in]
int. batchCount >= 0.

Number of matrices in the batch.

hipblasStatus_t hipblasSgetrfStridedBatched(hipblasHandle_t handle, const int n, float *A, const int lda, const hipblasStride strideA, int *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

hipblasStatus_t hipblasDgetrfStridedBatched(hipblasHandle_t handle, const int n, double *A, const int lda, const hipblasStride strideA, int *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

hipblasStatus_t hipblasCgetrfStridedBatched(hipblasHandle_t handle, const int n, hipblasComplex *A, const int lda, const hipblasStride strideA, int *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

hipblasStatus_t hipblasZgetrfStridedBatched(hipblasHandle_t handle, const int n, hipblasDoubleComplex *A, const int lda, const hipblasStride strideA, int *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

SOLVER API.

getrfStridedBatched computes the LU factorization of a batch of general n-by-n matrices using partial pivoting with row interchanges. The LU factorization can be done without pivoting if ipiv is passed as a nullptr.

In the case that ipiv is not null, the factorization of matrix $A_i$ in the batch has the form:

\[ A_i = P_iL_iU_i \]

where $P_i$ is a permutation matrix, $L_i$ is lower triangular with unit diagonal elements, and $U_i$ is upper triangular.

In the case that ipiv is null, the factorization is done without pivoting:

\[ A_i = L_iU_i \]

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
n – [in]
int. n >= 0.

The number of columns and rows of all matrices A_i in the batch.
A – [inout]
pointer to type. Array on the GPU (the size depends on the value of strideA).

On entry, the n-by-n matrices A_i to be factored. On exit, the factors L_i and U_i from the factorization. The unit diagonal elements of L_i are not stored.
lda – [in]
int. lda >= n.

Specifies the leading dimension of matrices A_i.
strideA – [in]
hipblasStride.

Stride from the start of one matrix A_i to the next one A_(i+1). There is no restriction for the value of strideA. Normal use case is strideA >= lda*n
ipiv – [out]
pointer to int. Array on the GPU (the size depends on the value of strideP).

Contains the vectors of pivots indices ipiv_i (corresponding to A_i). Dimension of ipiv_i is n. Elements of ipiv_i are 1-based indices. For each instance A_i in the batch and for 1 <= j <= n, the row j of the matrix A_i was interchanged with row ipiv_i[j]. Matrix P_i of the factorization can be derived from ipiv_i. The factorization here can be done without pivoting if ipiv is passed in as a nullptr.
strideP – [in]
hipblasStride.

Stride from the start of one vector ipiv_i to the next one ipiv_(i+1). There is no restriction for the value of strideP. Normal use case is strideP >= n.
info – [out]
pointer to int. Array of batchCount integers on the GPU.

If info[i] = 0, successful exit for factorization of A_i. If info[i] = j > 0, U_i is singular. U_i[j,j] is the first zero pivot.
batchCount – [in]
int. batchCount >= 0.

Number of matrices in the batch.

hipblasXgetrs + Batched, stridedBatched #

hipblasStatus_t hipblasSgetrs(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, float *A, const int lda, const int *ipiv, float *B, const int ldb, int *info)#

hipblasStatus_t hipblasDgetrs(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, double *A, const int lda, const int *ipiv, double *B, const int ldb, int *info)#

hipblasStatus_t hipblasCgetrs(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, hipblasComplex *A, const int lda, const int *ipiv, hipblasComplex *B, const int ldb, int *info)#

hipblasStatus_t hipblasZgetrs(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, hipblasDoubleComplex *A, const int lda, const int *ipiv, hipblasDoubleComplex *B, const int ldb, int *info)#

SOLVER API.

getrs solves a system of n linear equations on n variables in its factorized form.

It solves one of the following systems, depending on the value of trans:

\[\begin{split} \begin{array}{cl} A X = B & \: \text{not transposed,}\\ A^T X = B & \: \text{transposed, or}\\ A^H X = B & \: \text{conjugate transposed.} \end{array} \end{split}\]

Matrix A is defined by its triangular factors as returned by getrf.

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
trans – [in]
hipblasOperation_t.

Specifies the form of the system of equations.
n – [in]
int. n >= 0.

The order of the system, i.e. the number of columns and rows of A.
nrhs – [in]
int. nrhs >= 0.

The number of right hand sides, i.e., the number of columns of the matrix B.
A – [in]
pointer to type. Array on the GPU of dimension lda*n.

The factors L and U of the factorization A = P*L*U returned by
getrf.
lda – [in]
int. lda >= n.

The leading dimension of A.
ipiv – [in]
pointer to int. Array on the GPU of dimension n.

The pivot indices returned by
getrf.
B – [inout]
pointer to type. Array on the GPU of dimension ldb*nrhs.

On entry, the right hand side matrix B. On exit, the solution matrix X.
ldb – [in]
int. ldb >= n.

The leading dimension of B.
info – [out]
pointer to a int on the host.

If info = 0, successful exit. If info = j < 0, the j-th argument is invalid.

hipblasStatus_t hipblasSgetrsBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, float *const A[], const int lda, const int *ipiv, float *const B[], const int ldb, int *info, const int batchCount)#

hipblasStatus_t hipblasDgetrsBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, double *const A[], const int lda, const int *ipiv, double *const B[], const int ldb, int *info, const int batchCount)#

hipblasStatus_t hipblasCgetrsBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, hipblasComplex *const A[], const int lda, const int *ipiv, hipblasComplex *const B[], const int ldb, int *info, const int batchCount)#

hipblasStatus_t hipblasZgetrsBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, hipblasDoubleComplex *const A[], const int lda, const int *ipiv, hipblasDoubleComplex *const B[], const int ldb, int *info, const int batchCount)#

SOLVER API.

getrsBatched solves a batch of systems of n linear equations on n variables in its factorized forms.

For each instance i in the batch, it solves one of the following systems, depending on the value of trans:

\[\begin{split} \begin{array}{cl} A_i X_i = B_i & \: \text{not transposed,}\\ A_i^T X_i = B_i & \: \text{transposed, or}\\ A_i^H X_i = B_i & \: \text{conjugate transposed.} \end{array} \end{split}\]

Matrix $A_i$ is defined by its triangular factors as returned by getrfBatched.

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
trans – [in]
hipblasOperation_t.

Specifies the form of the system of equations of each instance in the batch.
n – [in]
int. n >= 0.

The order of the system, i.e. the number of columns and rows of all A_i matrices.
nrhs – [in]
int. nrhs >= 0.

The number of right hand sides, i.e., the number of columns of all the matrices B_i.
A – [in]
Array of pointers to type. Each pointer points to an array on the GPU of dimension lda*n.

The factors L_i and U_i of the factorization A_i = P_i*L_i*U_i returned by
getrfBatched.
lda – [in]
int. lda >= n.

The leading dimension of matrices A_i.
ipiv – [in]
pointer to int. Array on the GPU.

Contains the vectors ipiv_i of pivot indices returned by
getrfBatched.
B – [inout]
Array of pointers to type. Each pointer points to an array on the GPU of dimension ldb*nrhs.

On entry, the right hand side matrices B_i. On exit, the solution matrix X_i of each system in the batch.
ldb – [in]
int. ldb >= n.

The leading dimension of matrices B_i.
info – [out]
pointer to a int on the host.

If info = 0, successful exit. If info = j < 0, the j-th argument is invalid.
batchCount – [in]
int. batchCount >= 0.

Number of instances (systems) in the batch.

hipblasStatus_t hipblasSgetrsStridedBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, float *A, const int lda, const hipblasStride strideA, const int *ipiv, const hipblasStride strideP, float *B, const int ldb, const hipblasStride strideB, int *info, const int batchCount)#

hipblasStatus_t hipblasDgetrsStridedBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, double *A, const int lda, const hipblasStride strideA, const int *ipiv, const hipblasStride strideP, double *B, const int ldb, const hipblasStride strideB, int *info, const int batchCount)#

hipblasStatus_t hipblasCgetrsStridedBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, hipblasComplex *A, const int lda, const hipblasStride strideA, const int *ipiv, const hipblasStride strideP, hipblasComplex *B, const int ldb, const hipblasStride strideB, int *info, const int batchCount)#

hipblasStatus_t hipblasZgetrsStridedBatched(hipblasHandle_t handle, const hipblasOperation_t trans, const int n, const int nrhs, hipblasDoubleComplex *A, const int lda, const hipblasStride strideA, const int *ipiv, const hipblasStride strideP, hipblasDoubleComplex *B, const int ldb, const hipblasStride strideB, int *info, const int batchCount)#

SOLVER API.

getrsStridedBatched solves a batch of systems of n linear equations on n variables in its factorized forms.

For each instance i in the batch, it solves one of the following systems, depending on the value of trans:

\[\begin{split} \begin{array}{cl} A_i X_i = B_i & \: \text{not transposed,}\\ A_i^T X_i = B_i & \: \text{transposed, or}\\ A_i^H X_i = B_i & \: \text{conjugate transposed.} \end{array} \end{split}\]

Matrix $A_i$ is defined by its triangular factors as returned by getrfStridedBatched.

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] hipblasHandle_t.
trans – [in]
hipblasOperation_t.

Specifies the form of the system of equations of each instance in the batch.
n – [in]
int. n >= 0.

The order of the system, i.e. the number of columns and rows of all A_i matrices.
nrhs – [in]
int. nrhs >= 0.

The number of right hand sides, i.e., the number of columns of all the matrices B_i.
A – [in]
pointer to type. Array on the GPU (the size depends on the value of strideA).

The factors L_i and U_i of the factorization A_i = P_i*L_i*U_i returned by
getrfStridedBatched.
lda – [in]
int. lda >= n.

The leading dimension of matrices A_i.
strideA – [in]
hipblasStride.

Stride from the start of one matrix A_i to the next one A_(i+1). There is no restriction for the value of strideA. Normal use case is strideA >= lda*n.
ipiv – [in]
pointer to int. Array on the GPU (the size depends on the value of strideP).

Contains the vectors ipiv_i of pivot indices returned by
getrfStridedBatched.
strideP – [in]
hipblasStride.

Stride from the start of one vector ipiv_i to the next one ipiv_(i+1). There is no restriction for the value of strideP. Normal use case is strideP >= n.
B – [inout]
pointer to type. Array on the GPU (size depends on the value of strideB).

On entry, the right hand side matrices B_i. On exit, the solution matrix X_i of each system in the batch.
ldb – [in]
int. ldb >= n.

The leading dimension of matrices B_i.
strideB – [in]
hipblasStride.

Stride from the start of one matrix B_i to the next one B_(i+1). There is no restriction for the value of strideB. Normal use case is strideB >= ldb*nrhs.
info – [out]
pointer to a int on the host.

If info = 0, successful exit. If info = j < 0, the j-th argument is invalid.
batchCount – [in]
int. batchCount >= 0.

Number of instances (systems) in the batch.

hipblasXgetri + Batched, stridedBatched #

hipblasStatus_t hipblasSgetriBatched(hipblasHandle_t handle, const int n, float *const A[], const int lda, int *ipiv, float *const C[], const int ldc, int *info, const int batchCount)#

hipblasStatus_t hipblasDgetriBatched(hipblasHandle_t handle, const int n, double *const A[], const int lda, int *ipiv, double *const C[], const int ldc, int *info, const int batchCount)#

hipblasStatus_t hipblasCgetriBatched(hipblasHandle_t handle, const int n, hipblasComplex *const A[], const int lda, int *ipiv, hipblasComplex *const C[], const int ldc, int *info, const int batchCount)#

hipblasStatus_t hipblasZgetriBatched(hipblasHandle_t handle, const int n, hipblasDoubleComplex *const A[], const int lda, int *ipiv, hipblasDoubleComplex *const C[], const int ldc, int *info, const int batchCount)#

SOLVER API.

getriBatched computes the inverse $C_i = A_i^{-1}$ of a batch of general n-by-n matrices $A_i$.

The inverse is computed by solving the linear system

\[ A_i C_i = I \]

where I is the identity matrix, and $A_i$ is factorized as $A_i = P_i L_i U_i$ as given by getrfBatched.

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
n – [in]
int. n >= 0.

The number of rows and columns of all matrices A_i in the batch.
A – [in]
array of pointers to type. Each pointer points to an array on the GPU of dimension lda*n.

The factors L_i and U_i of the factorization A_i = P_i*L_i*U_i returned by
getrfBatched.
lda – [in]
int. lda >= n.

Specifies the leading dimension of matrices A_i.
ipiv – [in]
pointer to int. Array on the GPU (the size depends on the value of strideP).

The pivot indices returned by
getrfBatched. ipiv can be passed in as a nullptr, this will assume that getrfBatched was called without partial pivoting.
C – [out]
array of pointers to type. Each pointer points to an array on the GPU of dimension ldc*n.

If info[i] = 0, the inverse of matrices A_i. Otherwise, undefined.
ldc – [in]
int. ldc >= n.

Specifies the leading dimension of C_i.
info – [out]
pointer to int. Array of batchCount integers on the GPU.

If info[i] = 0, successful exit for inversion of A_i. If info[i] = j > 0, U_i is singular. U_i[j,j] is the first zero pivot.
batchCount – [in]
int. batchCount >= 0.

Number of matrices in the batch.

hipblasXgeqrf + Batched, stridedBatched #

hipblasStatus_t hipblasSgeqrf(hipblasHandle_t handle, const int m, const int n, float *A, const int lda, float *ipiv, int *info)#

hipblasStatus_t hipblasDgeqrf(hipblasHandle_t handle, const int m, const int n, double *A, const int lda, double *ipiv, int *info)#

hipblasStatus_t hipblasCgeqrf(hipblasHandle_t handle, const int m, const int n, hipblasComplex *A, const int lda, hipblasComplex *ipiv, int *info)#

hipblasStatus_t hipblasZgeqrf(hipblasHandle_t handle, const int m, const int n, hipblasDoubleComplex *A, const int lda, hipblasDoubleComplex *ipiv, int *info)#

SOLVER API.

geqrf computes a QR factorization of a general m-by-n matrix A.

The factorization has the form

\[\begin{split} A = Q\left[\begin{array}{c} R\\ 0 \end{array}\right] \end{split}\]

where R is upper triangular (upper trapezoidal if m < n), and Q is a m-by-m orthogonal/unitary matrix represented as the product of Householder matrices

\[ Q = H_1H_2\cdots H_k, \quad \text{with} \: k = \text{min}(m,n) \]

Each Householder matrix $H_i$ is given by

\[ H_i = I - \text{ipiv}[i] \cdot v_i v_i' \]

where the first i-1 elements of the Householder vector $v_i$ are zero, and $v_i[i] = 1$.

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
m – [in]
int. m >= 0.

The number of rows of the matrix A.
n – [in]
int. n >= 0.

The number of columns of the matrix A.
A – [inout]
pointer to type. Array on the GPU of dimension lda*n.

On entry, the m-by-n matrix to be factored. On exit, the elements on and above the diagonal contain the factor R; the elements below the diagonal are the last m - i elements of Householder vector v_i.
lda – [in]
int. lda >= m.

Specifies the leading dimension of A.
ipiv – [out]
pointer to type. Array on the GPU of dimension min(m,n).

The Householder scalars.
info – [out]
pointer to a int on the host.

If info = 0, successful exit. If info = j < 0, the j-th argument is invalid.

hipblasStatus_t hipblasSgeqrfBatched(hipblasHandle_t handle, const int m, const int n, float *const A[], const int lda, float *const ipiv[], int *info, const int batchCount)#

hipblasStatus_t hipblasDgeqrfBatched(hipblasHandle_t handle, const int m, const int n, double *const A[], const int lda, double *const ipiv[], int *info, const int batchCount)#

hipblasStatus_t hipblasCgeqrfBatched(hipblasHandle_t handle, const int m, const int n, hipblasComplex *const A[], const int lda, hipblasComplex *const ipiv[], int *info, const int batchCount)#

hipblasStatus_t hipblasZgeqrfBatched(hipblasHandle_t handle, const int m, const int n, hipblasDoubleComplex *const A[], const int lda, hipblasDoubleComplex *const ipiv[], int *info, const int batchCount)#

SOLVER API.

geqrfBatched computes the QR factorization of a batch of general m-by-n matrices.

The factorization of matrix $A_i$ in the batch has the form

\[\begin{split} A_i = Q_i\left[\begin{array}{c} R_i\\ 0 \end{array}\right] \end{split}\]

where $R_i$ is upper triangular (upper trapezoidal if m < n), and $Q_i$ is a m-by-m orthogonal/unitary matrix represented as the product of Householder matrices

\[ Q_i = H_{i_1}H_{i_2}\cdots H_{i_k}, \quad \text{with} \: k = \text{min}(m,n) \]

Each Householder matrix $H_{i_j}$ is given by

\[ H_{i_j} = I - \text{ipiv}_i[j] \cdot v_{i_j} v_{i_j}' \]

where the first j-1 elements of Householder vector $v_{i_j}$ are zero, and $v_{i_j}[j] = 1$.

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : s,d,c,z

Parameters:

handle – [in] hipblasHandle_t.
m – [in]
int. m >= 0.

The number of rows of all the matrices A_i in the batch.
n – [in]
int. n >= 0.

The number of columns of all the matrices A_i in the batch.
A – [inout]
Array of pointers to type. Each pointer points to an array on the GPU of dimension lda*n.

On entry, the m-by-n matrices A_i to be factored. On exit, the elements on and above the diagonal contain the factor R_i. The elements below the diagonal are the last m - j elements of Householder vector v_(i_j).
lda – [in]
int. lda >= m.

Specifies the leading dimension of matrices A_i.
ipiv – [out]
array of pointers to type. Each pointer points to an array on the GPU of dimension min(m, n).

Contains the vectors ipiv_i of corresponding Householder scalars.
info – [out]
pointer to a int on the host.

If info = 0, successful exit. If info = k < 0, the k-th argument is invalid.
batchCount – [in]
int. batchCount >= 0.

Number of matrices in the batch.

hipblasStatus_t hipblasSgeqrfStridedBatched(hipblasHandle_t handle, const int m, const int n, float *A, const int lda, const hipblasStride strideA, float *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

hipblasStatus_t hipblasDgeqrfStridedBatched(hipblasHandle_t handle, const int m, const int n, double *A, const int lda, const hipblasStride strideA, double *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

hipblasStatus_t hipblasCgeqrfStridedBatched(hipblasHandle_t handle, const int m, const int n, hipblasComplex *A, const int lda, const hipblasStride strideA, hipblasComplex *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

hipblasStatus_t hipblasZgeqrfStridedBatched(hipblasHandle_t handle, const int m, const int n, hipblasDoubleComplex *A, const int lda, const hipblasStride strideA, hipblasDoubleComplex *ipiv, const hipblasStride strideP, int *info, const int batchCount)#

SOLVER API.

geqrfStridedBatched computes the QR factorization of a batch of general m-by-n matrices.

The factorization of matrix $A_i$ in the batch has the form

\[\begin{split} A_i = Q_i\left[\begin{array}{c} R_i\\ 0 \end{array}\right] \end{split}\]

where $R_i$ is upper triangular (upper trapezoidal if m < n), and $Q_i$ is a m-by-m orthogonal/unitary matrix represented as the product of Householder matrices

\[ Q_i = H_{i_1}H_{i_2}\cdots H_{i_k}, \quad \text{with} \: k = \text{min}(m,n) \]

Each Householder matrix $H_{i_j}$ is given by

\[ H_{i_j} = I - \text{ipiv}_j[j] \cdot v_{i_j} v_{i_j}' \]

where the first j-1 elements of Householder vector $v_{i_j}$ are zero, and $v_{i_j}[j] = 1$.

Supported precisions in rocSOLVER : s,d,c,z
Supported precisions in cuBLAS : No support

Parameters:

handle – [in] hipblasHandle_t.
m – [in]
int. m >= 0.

The number of rows of all the matrices A_i in the batch.
n – [in]
int. n >= 0.

The number of columns of all the matrices A_i in the batch.
A – [inout]
pointer to type. Array on the GPU (the size depends on the value of strideA).

On entry, the m-by-n matrices A_i to be factored. On exit, the elements on and above the diagonal contain the factor R_i. The elements below the diagonal are the last m - j elements of Householder vector v_(i_j).
lda – [in]
int. lda >= m.

Specifies the leading dimension of matrices A_i.
strideA – [in]
hipblasStride.

Stride from the start of one matrix A_i to the next one A_(i+1). There is no restriction for the value of strideA. Normal use case is strideA >= lda*n.
ipiv – [out]
pointer to type. Array on the GPU (the size depends on the value of strideP).

Contains the vectors ipiv_i of corresponding Householder scalars.
strideP – [in]
hipblasStride.

Stride from the start of one vector ipiv_i to the next one ipiv_(i+1). There is no restriction for the value of strideP. Normal use is strideP >= min(m,n).
info – [out]
pointer to a int on the host.

If info = 0, successful exit. If info = k < 0, the k-th argument is invalid.
batchCount – [in]
int. batchCount >= 0.

Number of matrices in the batch.

hipblasXgels + Batched, StridedBatched #

Warning

doxygenfunction: Cannot find function “hipblasSgels” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasDgels” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasCgels” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasZgels” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasSgelsBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasDgelsBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasCgelsBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasZgelsBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasSgelsStridedBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasDgelsStridedBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasCgelsStridedBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

Warning

doxygenfunction: Cannot find function “hipblasZgelsStridedBatched” in doxygen xml output for project “hipBLAS Documentation” from directory: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-hipblas/checkouts/docs-5.3.3/docs/.doxygen/docBin/xml

BLAS Extensions#

hipblasGemmEx + Batched, StridedBatched #

hipblasStatus_t hipblasGemmEx(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const void *alpha, const void *A, hipblasDatatype_t aType, int lda, const void *B, hipblasDatatype_t bType, int ldb, const void *beta, void *C, hipblasDatatype_t cType, int ldc, hipblasDatatype_t computeType, hipblasGemmAlgo_t algo)#

BLAS EX API.

gemmEx performs one of the matrix-matrix operations

C = alpha*op( A )*op( B ) + beta*C,

where op( X ) is one of

op( X ) = X      or
op( X ) = X**T   or
op( X ) = X**H,

alpha and beta are scalars, and A, B, and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C is a m by n matrix.

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Note for int8 users - For rocBLAS backend, please read rocblas_gemm_ex documentation on int8 data layout requirements. hipBLAS makes the assumption that the data layout is in the preferred format for a given device as documented in rocBLAS.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A ).
transB – [in] [hipblasOperation_t] specifies the form of op( B ).
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
k – [in] [int] matrix dimension k.
alpha – [in] [const void *] device pointer or host pointer specifying the scalar alpha. Same datatype as computeType.
A – [in] [void *] device pointer storing matrix A.
aType – [in] [hipblasDatatype_t] specifies the datatype of matrix A.
lda – [in] [int] specifies the leading dimension of A.
B – [in] [void *] device pointer storing matrix B.
bType – [in] [hipblasDatatype_t] specifies the datatype of matrix B.
ldb – [in] [int] specifies the leading dimension of B.
beta – [in] [const void *] device pointer or host pointer specifying the scalar beta. Same datatype as computeType.
C – [in] [void *] device pointer storing matrix C.
cType – [in] [hipblasDatatype_t] specifies the datatype of matrix C.
ldc – [in] [int] specifies the leading dimension of C.
computeType – [in] [hipblasDatatype_t] specifies the datatype of computation.
algo – [in] [hipblasGemmAlgo_t] enumerant specifying the algorithm type.

hipblasStatus_t hipblasGemmBatchedEx(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const void *alpha, const void *A[], hipblasDatatype_t aType, int lda, const void *B[], hipblasDatatype_t bType, int ldb, const void *beta, void *C[], hipblasDatatype_t cType, int ldc, int batchCount, hipblasDatatype_t computeType, hipblasGemmAlgo_t algo)#

BLAS EX API.

gemmBatchedEx performs one of the batched matrix-matrix operations C_i = alpha*op(A_i)*op(B_i) + beta*C_i, for i = 1, …, batchCount. where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B, and C are batched pointers to matrices, with op( A ) an m by k by batchCount batched matrix, op( B ) a k by n by batchCount batched matrix and C a m by n by batchCount batched matrix. The batched matrices are an array of pointers to matrices. The number of pointers to matrices is batchCount.

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Note for int8 users - For rocBLAS backend, please read rocblas_gemm_batched_ex documentation on int8 data layout requirements. hipBLAS makes the assumption that the data layout is in the preferred format for a given device as documented in rocBLAS.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A ).
transB – [in] [hipblasOperation_t] specifies the form of op( B ).
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
k – [in] [int] matrix dimension k.
alpha – [in] [const void *] device pointer or host pointer specifying the scalar alpha. Same datatype as computeType.
A – [in] [void *] device pointer storing array of pointers to each matrix A_i.
aType – [in] [hipblasDatatype_t] specifies the datatype of each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i.
B – [in] [void *] device pointer storing array of pointers to each matrix B_i.
bType – [in] [hipblasDatatype_t] specifies the datatype of each matrix B_i.
ldb – [in] [int] specifies the leading dimension of each B_i.
beta – [in] [const void *] device pointer or host pointer specifying the scalar beta. Same datatype as computeType.
C – [in] [void *] device array of device pointers to each matrix C_i.
cType – [in] [hipblasDatatype_t] specifies the datatype of each matrix C_i.
ldc – [in] [int] specifies the leading dimension of each C_i.
batchCount – [in] [int] number of gemm operations in the batch.
computeType – [in] [hipblasDatatype_t] specifies the datatype of computation.
algo – [in] [hipblasGemmAlgo_t] enumerant specifying the algorithm type.

hipblasStatus_t hipblasGemmStridedBatchedEx(hipblasHandle_t handle, hipblasOperation_t transA, hipblasOperation_t transB, int m, int n, int k, const void *alpha, const void *A, hipblasDatatype_t aType, int lda, hipblasStride strideA, const void *B, hipblasDatatype_t bType, int ldb, hipblasStride strideB, const void *beta, void *C, hipblasDatatype_t cType, int ldc, hipblasStride strideC, int batchCount, hipblasDatatype_t computeType, hipblasGemmAlgo_t algo)#

BLAS EX API.

gemmStridedBatchedEx performs one of the strided_batched matrix-matrix operations

C_i = alpha*op(A_i)*op(B_i) + beta*C_i, for i = 1, ..., batchCount

where op( X ) is one of

op( X ) = X      or
op( X ) = X**T   or
op( X ) = X**H,

alpha and beta are scalars, and A, B, and C are strided_batched matrices, with op( A ) an m by k by batchCount strided_batched matrix, op( B ) a k by n by batchCount strided_batched matrix and C a m by n by batchCount strided_batched matrix.

The strided_batched matrices are multiple matrices separated by a constant stride. The number of matrices is batchCount.

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Note for int8 users - For rocBLAS backend, please read rocblas_gemm_strided_batched_ex documentation on int8 data layout requirements. hipBLAS makes the assumption that the data layout is in the preferred format for a given device as documented in rocBLAS.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
transA – [in] [hipblasOperation_t] specifies the form of op( A ).
transB – [in] [hipblasOperation_t] specifies the form of op( B ).
m – [in] [int] matrix dimension m.
n – [in] [int] matrix dimension n.
k – [in] [int] matrix dimension k.
alpha – [in] [const void *] device pointer or host pointer specifying the scalar alpha. Same datatype as computeType.
A – [in] [void *] device pointer pointing to first matrix A_1.
aType – [in] [hipblasDatatype_t] specifies the datatype of each matrix A_i.
lda – [in] [int] specifies the leading dimension of each A_i.
strideA – [in] [hipblasStride] specifies stride from start of one A_i matrix to the next A_(i + 1).
B – [in] [void *] device pointer pointing to first matrix B_1.
bType – [in] [hipblasDatatype_t] specifies the datatype of each matrix B_i.
ldb – [in] [int] specifies the leading dimension of each B_i.
strideB – [in] [hipblasStride] specifies stride from start of one B_i matrix to the next B_(i + 1).
beta – [in] [const void *] device pointer or host pointer specifying the scalar beta. Same datatype as computeType.
C – [in] [void *] device pointer pointing to first matrix C_1.
cType – [in] [hipblasDatatype_t] specifies the datatype of each matrix C_i.
ldc – [in] [int] specifies the leading dimension of each C_i.
strideC – [in] [hipblasStride] specifies stride from start of one C_i matrix to the next C_(i + 1).
batchCount – [in] [int] number of gemm operations in the batch.
computeType – [in] [hipblasDatatype_t] specifies the datatype of computation.
algo – [in] [hipblasGemmAlgo_t] enumerant specifying the algorithm type.

hipblasTrsmEx + Batched, StridedBatched #

hipblasStatus_t hipblasTrsmEx(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const void *alpha, void *A, int lda, void *B, int ldb, const void *invA, int invAsize, hipblasDatatype_t computeType)#

BLAS EX API

trsmEx solves

op(A)*X = alpha*B or X*op(A) = alpha*B,

where alpha is a scalar, X and B are m by n matrices, A is triangular matrix and op(A) is one of

op( A ) = A   or   op( A ) = A^T   or   op( A ) = A^H.

The matrix X is overwritten on B.

This function gives the user the ability to reuse the invA matrix between runs. If invA == NULL, hipblasTrsmEx will automatically calculate invA on every run.

Setting up invA: The accepted invA matrix consists of the packed 128x128 inverses of the diagonal blocks of matrix A, followed by any smaller diagonal block that remains. To set up invA it is recommended that hipblasTrtriBatched be used with matrix A as the input.

Device memory of size 128 x k should be allocated for invA ahead of time, where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT. The actual number of elements in invA should be passed as invAsize.

To begin, hipblasTrtriBatched must be called on the full 128x128 sized diagonal blocks of matrix A. Below are the restricted parameters:

n = 128
ldinvA = 128
stride_invA = 128x128
batchCount = k / 128,

Then any remaining block may be added:

n = k % 128
invA = invA + stride_invA * previousBatchCount
ldinvA = 128
batchCount = 1

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: op(A)*X = alpha*B. HIPBLAS_SIDE_RIGHT: X*op(A) = alpha*B.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: A is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: A is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: op(A) = A. HIPBLAS_OP_T: op(A) = A^T. HIPBLAS_ON_C: op(A) = A^H.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: A is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: A is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of B. m >= 0.
n – [in] [int] n specifies the number of columns of B. n >= 0.
alpha – [in] [void *] device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced, and B need not be set before entry.
A – [in] [void *] device pointer storing matrix A. of dimension ( lda, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side = HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
B – [inout] [void *] device pointer storing matrix B. B is of dimension ( ldb, n ). Before entry, the leading m by n part of the array B must contain the right-hand side matrix B, and on exit is overwritten by the solution matrix X.
ldb – [in] [int] ldb specifies the first dimension of B. ldb >= max( 1, m ).
invA – [in] [void *] device pointer storing the inverse diagonal blocks of A. invA is of dimension ( ld_invA, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT. ld_invA must be equal to 128.
invAsize – [in] [int] invAsize specifies the number of elements of device memory in invA.
computeType – [in] [hipblasDatatype_t] specifies the datatype of computation

hipblasStatus_t hipblasTrsmBatchedEx(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const void *alpha, void *A, int lda, void *B, int ldb, int batchCount, const void *invA, int invAsize, hipblasDatatype_t computeType)#

BLAS EX API

trsmBatchedEx solves

op(A_i)*X_i = alpha*B_i or X_i*op(A_i) = alpha*B_i,

for i = 1, …, batchCount; and where alpha is a scalar, X and B are arrays of m by n matrices, A is an array of triangular matrix and each op(A_i) is one of

op( A_i ) = A_i   or   op( A_i ) = A_i^T   or   op( A_i ) = A_i^H.

Each matrix X_i is overwritten on B_i.

This function gives the user the ability to reuse the invA matrix between runs. If invA == NULL, hipblasTrsmBatchedEx will automatically calculate each invA_i on every run.

Setting up invA: Each accepted invA_i matrix consists of the packed 128x128 inverses of the diagonal blocks of matrix A_i, followed by any smaller diagonal block that remains. To set up each invA_i it is recommended that hipblasTrtriBatched be used with matrix A_i as the input. invA is an array of pointers of batchCount length holding each invA_i.

Device memory of size 128 x k should be allocated for each invA_i ahead of time, where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT. The actual number of elements in each invA_i should be passed as invAsize.

To begin, hipblasTrtriBatched must be called on the full 128x128 sized diagonal blocks of each matrix A_i. Below are the restricted parameters:

n = 128
ldinvA = 128
stride_invA = 128x128
batchCount = k / 128,

Then any remaining block may be added:

n = k % 128
invA = invA + stride_invA * previousBatchCount
ldinvA = 128
batchCount = 1

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: op(A)*X = alpha*B. HIPBLAS_SIDE_RIGHT: X*op(A) = alpha*B.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: op(A) = A. HIPBLAS_OP_T: op(A) = A^T. HIPBLAS_OP_C: op(A) = A^H.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of each B_i. m >= 0.
n – [in] [int] n specifies the number of columns of each B_i. n >= 0.
alpha – [in] [void *] device pointer or host pointer alpha specifying the scalar alpha. When alpha is &zero then A is not referenced, and B need not be set before entry.
A – [in] [void *] device array of device pointers storing each matrix A_i. each A_i is of dimension ( lda, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of each A_i. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side = HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
B – [inout] [void *] device array of device pointers storing each matrix B_i. each B_i is of dimension ( ldb, n ). Before entry, the leading m by n part of the array B_i must contain the right-hand side matrix B_i, and on exit is overwritten by the solution matrix X_i
ldb – [in] [int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ).
batchCount – [in] [int] specifies how many batches.
invA – [in] [void *] device array of device pointers storing the inverse diagonal blocks of each A_i. each invA_i is of dimension ( ld_invA, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT. ld_invA must be equal to 128.
invAsize – [in] [int] invAsize specifies the number of elements of device memory in each invA_i.
computeType – [in] [hipblasDatatype_t] specifies the datatype of computation

hipblasStatus_t hipblasTrsmStridedBatchedEx(hipblasHandle_t handle, hipblasSideMode_t side, hipblasFillMode_t uplo, hipblasOperation_t transA, hipblasDiagType_t diag, int m, int n, const void *alpha, void *A, int lda, hipblasStride strideA, void *B, int ldb, hipblasStride strideB, int batchCount, const void *invA, int invAsize, hipblasStride strideInvA, hipblasDatatype_t computeType)#

BLAS EX API

trsmStridedBatchedEx solves

op(A_i)*X_i = alpha*B_i or X_i*op(A_i) = alpha*B_i,

for i = 1, …, batchCount; and where alpha is a scalar, X and B are strided batched m by n matrices, A is a strided batched triangular matrix and op(A_i) is one of

op( A_i ) = A_i   or   op( A_i ) = A_i^T   or   op( A_i ) = A_i^H.

Each matrix X_i is overwritten on B_i.

This function gives the user the ability to reuse each invA_i matrix between runs. If invA == NULL, hipblasTrsmStridedBatchedEx will automatically calculate each invA_i on every run.

Setting up invA: Each accepted invA_i matrix consists of the packed 128x128 inverses of the diagonal blocks of matrix A_i, followed by any smaller diagonal block that remains. To set up invA_i it is recommended that hipblasTrtriBatched be used with matrix A_i as the input. invA is a contiguous piece of memory holding each invA_i.

Device memory of size 128 x k should be allocated for each invA_i ahead of time, where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT. The actual number of elements in each invA_i should be passed as invAsize.

To begin, hipblasTrtriBatched must be called on the full 128x128 sized diagonal blocks of each matrix A_i. Below are the restricted parameters:

n = 128
ldinvA = 128
stride_invA = 128x128
batchCount = k / 128,

Then any remaining block may be added:

n = k % 128
invA = invA + stride_invA * previousBatchCount
ldinvA = 128
batchCount = 1

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
side – [in] [hipblasSideMode_t] HIPBLAS_SIDE_LEFT: op(A)*X = alpha*B. HIPBLAS_SIDE_RIGHT: X*op(A) = alpha*B.
uplo – [in] [hipblasFillMode_t] HIPBLAS_FILL_MODE_UPPER: each A_i is an upper triangular matrix. HIPBLAS_FILL_MODE_LOWER: each A_i is a lower triangular matrix.
transA – [in] [hipblasOperation_t] HIPBLAS_OP_N: op(A) = A. HIPBLAS_OP_T: op(A) = A^T. HIPBLAS_OP_C: op(A) = A^H.
diag – [in] [hipblasDiagType_t] HIPBLAS_DIAG_UNIT: each A_i is assumed to be unit triangular. HIPBLAS_DIAG_NON_UNIT: each A_i is not assumed to be unit triangular.
m – [in] [int] m specifies the number of rows of each B_i. m >= 0.
n – [in] [int] n specifies the number of columns of each B_i. n >= 0.
alpha – [in] [void *] device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced, and B need not be set before entry.
A – [in] [void *] device pointer storing matrix A. of dimension ( lda, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT only the upper/lower triangular part is accessed.
lda – [in] [int] lda specifies the first dimension of A. if side = HIPBLAS_SIDE_LEFT, lda >= max( 1, m ), if side = HIPBLAS_SIDE_RIGHT, lda >= max( 1, n ).
strideA – [in] [hipblasStride] The stride between each A matrix.
B – [inout] [void *] device pointer pointing to first matrix B_i. each B_i is of dimension ( ldb, n ). Before entry, the leading m by n part of each array B_i must contain the right-hand side of matrix B_i, and on exit is overwritten by the solution matrix X_i.
ldb – [in] [int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ).
strideB – [in] [hipblasStride] The stride between each B_i matrix.
batchCount – [in] [int] specifies how many batches.
invA – [in] [void *] device pointer storing the inverse diagonal blocks of each A_i. invA points to the first invA_1. each invA_i is of dimension ( ld_invA, k ), where k is m when HIPBLAS_SIDE_LEFT and is n when HIPBLAS_SIDE_RIGHT. ld_invA must be equal to 128.
invAsize – [in] [int] invAsize specifies the number of elements of device memory in each invA_i.
strideInvA – [in] [hipblasStride] The stride between each invA matrix.
computeType – [in] [hipblasDatatype_t] specifies the datatype of computation

hipblasAxpyEx + Batched, StridedBatched #

hipblasStatus_t hipblasAxpyEx(hipblasHandle_t handle, int n, const void *alpha, hipblasDatatype_t alphaType, const void *x, hipblasDatatype_t xType, int incx, void *y, hipblasDatatype_t yType, int incy, hipblasDatatype_t executionType)#

BLAS EX API.

axpyEx computes constant alpha multiplied by vector x, plus vector y

y := alpha * x + y

- Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
alpha – [in] device pointer or host pointer to specify the scalar alpha.
alphaType – [in] [hipblasDatatype_t] specifies the datatype of alpha.
x – [in] device pointer storing vector x.
xType – [in] [hipblasDatatype_t] specifies the datatype of vector x.
incx – [in] [int] specifies the increment for the elements of x.
y – [inout] device pointer storing vector y.
yType – [in] [hipblasDatatype_t] specifies the datatype of vector y.
incy – [in] [int] specifies the increment for the elements of y.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasAxpyBatchedEx(hipblasHandle_t handle, int n, const void *alpha, hipblasDatatype_t alphaType, const void *x, hipblasDatatype_t xType, int incx, void *y, hipblasDatatype_t yType, int incy, int batchCount, hipblasDatatype_t executionType)#

BLAS EX API.

axpyBatchedEx computes constant alpha multiplied by vector x, plus vector y over a set of batched vectors.

y := alpha * x + y

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
alpha – [in] device pointer or host pointer to specify the scalar alpha.
alphaType – [in] [hipblasDatatype_t] specifies the datatype of alpha.
x – [in] device array of device pointers storing each vector x_i.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [inout] device array of device pointers storing each vector y_i.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
batchCount – [in] [int] number of instances in the batch.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasAxpyStridedBatchedEx(hipblasHandle_t handle, int n, const void *alpha, hipblasDatatype_t alphaType, const void *x, hipblasDatatype_t xType, int incx, hipblasStride stridex, void *y, hipblasDatatype_t yType, int incy, hipblasStride stridey, int batchCount, hipblasDatatype_t executionType)#

BLAS EX API.

axpyStridedBatchedEx computes constant alpha multiplied by vector x, plus vector y over a set of strided batched vectors.

y := alpha * x + y

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
alpha – [in] device pointer or host pointer to specify the scalar alpha.
alphaType – [in] [hipblasDatatype_t] specifies the datatype of alpha.
x – [in] device pointer to the first vector x_1.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) to the next one (x_i+1). There are no restrictions placed on stridex, however the user should take care to ensure that stridex is of appropriate size, for a typical case this means stridex >= n * incx.
y – [inout] device pointer to the first vector y_1.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) to the next one (y_i+1). There are no restrictions placed on stridey, however the user should take care to ensure that stridey is of appropriate size, for a typical case this means stridey >= n * incy.
batchCount – [in] [int] number of instances in the batch.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasDotEx + Batched, StridedBatched #

hipblasStatus_t hipblasDotEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, const void *y, hipblasDatatype_t yType, int incy, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS EX API.

dotEx performs the dot product of vectors x and y

result = x * y;

dotcEx performs the dot product of the conjugate of complex vector x and complex vector y

result = conjugate (x) * y;

- Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
x – [in] device pointer storing vector x.
xType – [in] [hipblasDatatype_t] specifies the datatype of vector x.
incx – [in] [int] specifies the increment for the elements of y.
y – [in] device pointer storing vector y.
yType – [in] [hipblasDatatype_t] specifies the datatype of vector y.
incy – [in] [int] specifies the increment for the elements of y.
result – [inout] device pointer or host pointer to store the dot product. return is 0.0 if n <= 0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasDotBatchedEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, const void *y, hipblasDatatype_t yType, int incy, int batchCount, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS EX API.

dotBatchedEx performs a batch of dot products of vectors x and y

result_i = x_i * y_i;

dotcBatchedEx performs a batch of dot products of the conjugate of complex vector x and complex vector y

result_i = conjugate (x_i) * y_i;

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, …, batchCount

- Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [in] device array of device pointers storing each vector x_i.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [in] device array of device pointers storing each vector y_i.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
batchCount – [in] [int] number of instances in the batch
result – [inout] device array or host array of batchCount size to store the dot products of each batch. return 0.0 for each element if n <= 0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasDotStridedBatchedEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, hipblasStride stridex, const void *y, hipblasDatatype_t yType, int incy, hipblasStride stridey, int batchCount, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS EX API.

dotStridedBatchedEx performs a batch of dot products of vectors x and y

result_i = x_i * y_i;

dotc_strided_batched_ex performs a batch of dot products of the conjugate of complex vector x and complex vector y

result_i = conjugate (x_i) * y_i;

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, …, batchCount

- Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [in] device pointer to the first vector (x_1) in the batch.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1)
y – [in] device pointer to the first vector (y_1) in the batch.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1)
batchCount – [in] [int] number of instances in the batch
result – [inout] device array or host array of batchCount size to store the dot products of each batch. return 0.0 for each element if n <= 0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasDotcEx + Batched, StridedBatched #

hipblasStatus_t hipblasDotcEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, const void *y, hipblasDatatype_t yType, int incy, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS EX API.

dotEx performs the dot product of vectors x and y

result = x * y;

dotcEx performs the dot product of the conjugate of complex vector x and complex vector y

result = conjugate (x) * y;

- Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x and y.
x – [in] device pointer storing vector x.
xType – [in] [hipblasDatatype_t] specifies the datatype of vector x.
incx – [in] [int] specifies the increment for the elements of y.
y – [in] device pointer storing vector y.
yType – [in] [hipblasDatatype_t] specifies the datatype of vector y.
incy – [in] [int] specifies the increment for the elements of y.
result – [inout] device pointer or host pointer to store the dot product. return is 0.0 if n <= 0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasDotcBatchedEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, const void *y, hipblasDatatype_t yType, int incy, int batchCount, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS EX API.

dotBatchedEx performs a batch of dot products of vectors x and y

result_i = x_i * y_i;

dotcBatchedEx performs a batch of dot products of the conjugate of complex vector x and complex vector y

result_i = conjugate (x_i) * y_i;

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, …, batchCount

- Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [in] device array of device pointers storing each vector x_i.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
y – [in] device array of device pointers storing each vector y_i.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
batchCount – [in] [int] number of instances in the batch
result – [inout] device array or host array of batchCount size to store the dot products of each batch. return 0.0 for each element if n <= 0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasDotcStridedBatchedEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, hipblasStride stridex, const void *y, hipblasDatatype_t yType, int incy, hipblasStride stridey, int batchCount, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS EX API.

dotStridedBatchedEx performs a batch of dot products of vectors x and y

result_i = x_i * y_i;

dotc_strided_batched_ex performs a batch of dot products of the conjugate of complex vector x and complex vector y

result_i = conjugate (x_i) * y_i;

where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, …, batchCount

- Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in each x_i and y_i.
x – [in] device pointer to the first vector (x_1) in the batch.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1)
y – [in] device pointer to the first vector (y_1) in the batch.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment for the elements of each y_i.
stridey – [in] [hipblasStride] stride from the start of one vector (y_i) and the next one (y_i+1)
batchCount – [in] [int] number of instances in the batch
result – [inout] device array or host array of batchCount size to store the dot products of each batch. return 0.0 for each element if n <= 0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasNrm2Ex + Batched, StridedBatched #

hipblasStatus_t hipblasNrm2Ex(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS_EX API.

nrm2Ex computes the euclidean norm of a real or complex vector

      result := sqrt( x'*x ) for real vectors
      result := sqrt( x**H*x ) for complex vectors

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
x – [in] device pointer storing vector x.
xType – [in] [hipblasDatatype_t] specifies the datatype of the vector x.
incx – [in] [int] specifies the increment for the elements of y.
result – [inout] device pointer or host pointer to store the nrm2 product. return is 0.0 if n, incx<=0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasNrm2BatchedEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, int batchCount, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS_EX API.

nrm2BatchedEx computes the euclidean norm over a batch of real or complex vectors

      result := sqrt( x_i'*x_i ) for real vectors x, for i = 1, ..., batchCount
      result := sqrt( x_i**H*x_i ) for complex vectors x, for i = 1, ..., batchCount

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i.
x – [in] device array of device pointers storing each vector x_i.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
batchCount – [in] [int] number of instances in the batch
result – [out] device pointer or host pointer to array of batchCount size for nrm2 results. return is 0.0 for each element if n <= 0, incx<=0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasNrm2StridedBatchedEx(hipblasHandle_t handle, int n, const void *x, hipblasDatatype_t xType, int incx, hipblasStride stridex, int batchCount, void *result, hipblasDatatype_t resultType, hipblasDatatype_t executionType)#

BLAS_EX API.

nrm2StridedBatchedEx computes the euclidean norm over a batch of real or complex vectors

      := sqrt( x_i'*x_i ) for real vectors x, for i = 1, ..., batchCount
      := sqrt( x_i**H*x_i ) for complex vectors, for i = 1, ..., batchCount

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i.
x – [in] device pointer to the first vector x_1.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i. incx must be > 0.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) and the next one (x_i+1). There are no restrictions placed on stride_x, however the user should take care to ensure that stride_x is of appropriate size, for a typical case this means stride_x >= n * incx.
batchCount – [in] [int] number of instances in the batch
result – [out] device pointer or host pointer to array for storing contiguous batchCount results. return is 0.0 for each element if n <= 0, incx<=0.
resultType – [in] [hipblasDatatype_t] specifies the datatype of the result.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasRotEx + Batched, StridedBatched #

hipblasStatus_t hipblasRotEx(hipblasHandle_t handle, int n, void *x, hipblasDatatype_t xType, int incx, void *y, hipblasDatatype_t yType, int incy, const void *c, const void *s, hipblasDatatype_t csType, hipblasDatatype_t executionType)#

BLAS EX API.

rotEx applies the Givens rotation matrix defined by c=cos(alpha) and s=sin(alpha) to vectors x and y. Scalars c and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

In the case where cs_type is real: x := c * x + s * y y := c * y - s * x

In the case where cs_type is complex, the imaginary part of c is ignored: x := real(c) * x + s * y y := real(c) * y - conj(s) * x

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in the x and y vectors.
x – [inout] device pointer storing vector x.
xType – [in] [hipblasDatatype_t] specifies the datatype of vector x.
incx – [in] [int] specifies the increment between elements of x.
y – [inout] device pointer storing vector y.
yType – [in] [hipblasDatatype_t] specifies the datatype of vector y.
incy – [in] [int] specifies the increment between elements of y.
c – [in] device pointer or host pointer storing scalar cosine component of the rotation matrix.
s – [in] device pointer or host pointer storing scalar sine component of the rotation matrix.
csType – [in] [hipblasDatatype_t] specifies the datatype of c and s.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasRotBatchedEx(hipblasHandle_t handle, int n, void *x, hipblasDatatype_t xType, int incx, void *y, hipblasDatatype_t yType, int incy, const void *c, const void *s, hipblasDatatype_t csType, int batchCount, hipblasDatatype_t executionType)#

BLAS EX API.

rotBatchedEx applies the Givens rotation matrix defined by c=cos(alpha) and s=sin(alpha) to batched vectors x_i and y_i, for i = 1, …, batchCount. Scalars c and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

In the case where cs_type is real: x := c * x + s * y y := c * y - s * x

In the case where cs_type is complex, the imaginary part of c is ignored: x := real(c) * x + s * y y := real(c) * y - conj(s) * x

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i and y_i vectors.
x – [inout] device array of deivce pointers storing each vector x_i.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment between elements of each x_i.
y – [inout] device array of device pointers storing each vector y_i.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment between elements of each y_i.
c – [in] device pointer or host pointer to scalar cosine component of the rotation matrix.
s – [in] device pointer or host pointer to scalar sine component of the rotation matrix.
csType – [in] [hipblasDatatype_t] specifies the datatype of c and s.
batchCount – [in] [int] the number of x and y arrays, i.e. the number of batches.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasRotStridedBatchedEx(hipblasHandle_t handle, int n, void *x, hipblasDatatype_t xType, int incx, hipblasStride stridex, void *y, hipblasDatatype_t yType, int incy, hipblasStride stridey, const void *c, const void *s, hipblasDatatype_t csType, int batchCount, hipblasDatatype_t executionType)#

BLAS Level 1 API.

rotStridedBatchedEx applies the Givens rotation matrix defined by c=cos(alpha) and s=sin(alpha) to strided batched vectors x_i and y_i, for i = 1, …, batchCount. Scalars c and s may be stored in either host or device memory, location is specified by calling hipblasSetPointerMode.

In the case where cs_type is real: x := c * x + s * y y := c * y - s * x

In the case where cs_type is complex, the imaginary part of c is ignored: x := real(c) * x + s * y y := real(c) * y - conj(s) * x

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] number of elements in each x_i and y_i vectors.
x – [inout] device pointer to the first vector x_1.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment between elements of each x_i.
stridex – [in] [hipblasStride] specifies the increment from the beginning of x_i to the beginning of x_(i+1)
y – [inout] device pointer to the first vector y_1.
yType – [in] [hipblasDatatype_t] specifies the datatype of each vector y_i.
incy – [in] [int] specifies the increment between elements of each y_i.
stridey – [in] [hipblasStride] specifies the increment from the beginning of y_i to the beginning of y_(i+1)
c – [in] device pointer or host pointer to scalar cosine component of the rotation matrix.
s – [in] device pointer or host pointer to scalar sine component of the rotation matrix.
csType – [in] [hipblasDatatype_t] specifies the datatype of c and s.
batchCount – [in] [int] the number of x and y arrays, i.e. the number of batches.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasScalEx + Batched, StridedBatched #

hipblasStatus_t hipblasScalEx(hipblasHandle_t handle, int n, const void *alpha, hipblasDatatype_t alphaType, void *x, hipblasDatatype_t xType, int incx, hipblasDatatype_t executionType)#

BLAS EX API.

scalEx scales each element of vector x with scalar alpha.

x := alpha * x

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
alpha – [in] device pointer or host pointer for the scalar alpha.
alphaType – [in] [hipblasDatatype_t] specifies the datatype of alpha.
x – [inout] device pointer storing vector x.
xType – [in] [hipblasDatatype_t] specifies the datatype of vector x.
incx – [in] [int] specifies the increment for the elements of x.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasScalBatchedEx(hipblasHandle_t handle, int n, const void *alpha, hipblasDatatype_t alphaType, void *x, hipblasDatatype_t xType, int incx, int batchCount, hipblasDatatype_t executionType)#

BLAS EX API.

scalBatchedEx scales each element of each vector x_i with scalar alpha.

x_i := alpha * x_i

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
alpha – [in] device pointer or host pointer for the scalar alpha.
alphaType – [in] [hipblasDatatype_t] specifies the datatype of alpha.
x – [inout] device array of device pointers storing each vector x_i.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
batchCount – [in] [int] number of instances in the batch.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

hipblasStatus_t hipblasScalStridedBatchedEx(hipblasHandle_t handle, int n, const void *alpha, hipblasDatatype_t alphaType, void *x, hipblasDatatype_t xType, int incx, hipblasStride stridex, int batchCount, hipblasDatatype_t executionType)#

BLAS EX API.

scalStridedBatchedEx scales each element of vector x with scalar alpha over a set of strided batched vectors.

x := alpha * x

Supported types are determined by the backend. See rocBLAS/cuBLAS documentation.

Parameters:

handle – [in] [hipblasHandle_t] handle to the hipblas library context queue.
n – [in] [int] the number of elements in x.
alpha – [in] device pointer or host pointer for the scalar alpha.
alphaType – [in] [hipblasDatatype_t] specifies the datatype of alpha.
x – [inout] device pointer to the first vector x_1.
xType – [in] [hipblasDatatype_t] specifies the datatype of each vector x_i.
incx – [in] [int] specifies the increment for the elements of each x_i.
stridex – [in] [hipblasStride] stride from the start of one vector (x_i) to the next one (x_i+1). There are no restrictions placed on stridex, however the user should take care to ensure that stridex is of appropriate size, for a typical case this means stridex >= n * incx.
batchCount – [in] [int] number of instances in the batch.
executionType – [in] [hipblasDatatype_t] specifies the datatype of computation.

Auxiliary#

hipblasCreate#

hipblasStatus_t hipblasCreate(hipblasHandle_t *handle)#: Create hipblas handle.

hipblasDestroy#

hipblasStatus_t hipblasDestroy(hipblasHandle_t handle)#: Destroys the library context created using hipblasCreate()

hipblasSetStream#

hipblasStatus_t hipblasSetStream(hipblasHandle_t handle, hipStream_t streamId)#: Set stream for handle.

hipblasGetStream#

hipblasStatus_t hipblasGetStream(hipblasHandle_t handle, hipStream_t *streamId)#: Get stream[0] for handle.

hipblasSetPointerMode#

hipblasStatus_t hipblasSetPointerMode(hipblasHandle_t handle, hipblasPointerMode_t mode)#: Set hipblas pointer mode.

hipblasGetPointerMode#

hipblasStatus_t hipblasGetPointerMode(hipblasHandle_t handle, hipblasPointerMode_t *mode)#: Get hipblas pointer mode.

hipblasSetVector#

hipblasStatus_t hipblasSetVector(int n, int elemSize, const void *x, int incx, void *y, int incy)#

copy vector from host to device

Parameters:

n – [in] [int] number of elements in the vector
elemSize – [in] [int] Size of both vectors in bytes
x – [in] pointer to vector on the host
incx – [in] [int] specifies the increment for the elements of the vector
y – [out] pointer to vector on the device
incy – [in] [int] specifies the increment for the elements of the vector

hipblasGetVector#

hipblasStatus_t hipblasGetVector(int n, int elemSize, const void *x, int incx, void *y, int incy)#

copy vector from device to host

Parameters:

n – [in] [int] number of elements in the vector
elemSize – [in] [int] Size of both vectors in bytes
x – [in] pointer to vector on the device
incx – [in] [int] specifies the increment for the elements of the vector
y – [out] pointer to vector on the host
incy – [in] [int] specifies the increment for the elements of the vector

hipblasSetMatrix#

hipblasStatus_t hipblasSetMatrix(int rows, int cols, int elemSize, const void *AP, int lda, void *BP, int ldb)#

copy matrix from host to device

Parameters:

rows – [in] [int] number of rows in matrices
cols – [in] [int] number of columns in matrices
elemSize – [in] [int] number of bytes per element in the matrix
AP – [in] pointer to matrix on the host
lda – [in] [int] specifies the leading dimension of A, lda >= rows
BP – [out] pointer to matrix on the GPU
ldb – [in] [int] specifies the leading dimension of B, ldb >= rows

hipblasGetMatrix#

hipblasStatus_t hipblasGetMatrix(int rows, int cols, int elemSize, const void *AP, int lda, void *BP, int ldb)#

copy matrix from device to host

Parameters:

rows – [in] [int] number of rows in matrices
cols – [in] [int] number of columns in matrices
elemSize – [in] [int] number of bytes per element in the matrix
AP – [in] pointer to matrix on the GPU
lda – [in] [int] specifies the leading dimension of A, lda >= rows
BP – [out] pointer to matrix on the host
ldb – [in] [int] specifies the leading dimension of B, ldb >= rows

hipblasSetVectorAsync#

hipblasStatus_t hipblasSetVectorAsync(int n, int elemSize, const void *x, int incx, void *y, int incy, hipStream_t stream)#

asynchronously copy vector from host to device

hipblasSetVectorAsync copies a vector from pinned host memory to device memory asynchronously. Memory on the host must be allocated with hipHostMalloc or the transfer will be synchronous.

Parameters:

n – [in] [int] number of elements in the vector
elemSize – [in] [int] number of bytes per element in the matrix
x – [in] pointer to vector on the host
incx – [in] [int] specifies the increment for the elements of the vector
y – [out] pointer to vector on the device
incy – [in] [int] specifies the increment for the elements of the vector
stream – [in] specifies the stream into which this transfer request is queued

hipblasGetVectorAsync#

hipblasStatus_t hipblasGetVectorAsync(int n, int elemSize, const void *x, int incx, void *y, int incy, hipStream_t stream)#

asynchronously copy vector from device to host

hipblasGetVectorAsync copies a vector from pinned host memory to device memory asynchronously. Memory on the host must be allocated with hipHostMalloc or the transfer will be synchronous.

Parameters:

n – [in] [int] number of elements in the vector
elemSize – [in] [int] number of bytes per element in the matrix
x – [in] pointer to vector on the device
incx – [in] [int] specifies the increment for the elements of the vector
y – [out] pointer to vector on the host
incy – [in] [int] specifies the increment for the elements of the vector
stream – [in] specifies the stream into which this transfer request is queued

hipblasSetMatrixAsync#

hipblasStatus_t hipblasSetMatrixAsync(int rows, int cols, int elemSize, const void *AP, int lda, void *BP, int ldb, hipStream_t stream)#

asynchronously copy matrix from host to device

hipblasSetMatrixAsync copies a matrix from pinned host memory to device memory asynchronously. Memory on the host must be allocated with hipHostMalloc or the transfer will be synchronous.

Parameters:

rows – [in] [int] number of rows in matrices
cols – [in] [int] number of columns in matrices
elemSize – [in] [int] number of bytes per element in the matrix
AP – [in] pointer to matrix on the host
lda – [in] [int] specifies the leading dimension of A, lda >= rows
BP – [out] pointer to matrix on the GPU
ldb – [in] [int] specifies the leading dimension of B, ldb >= rows
stream – [in] specifies the stream into which this transfer request is queued

hipblasGetMatrixAsync#

hipblasStatus_t hipblasGetMatrixAsync(int rows, int cols, int elemSize, const void *AP, int lda, void *BP, int ldb, hipStream_t stream)#

asynchronously copy matrix from device to host

hipblasGetMatrixAsync copies a matrix from device memory to pinned host memory asynchronously. Memory on the host must be allocated with hipHostMalloc or the transfer will be synchronous.

Parameters:

rows – [in] [int] number of rows in matrices
cols – [in] [int] number of columns in matrices
elemSize – [in] [int] number of bytes per element in the matrix
AP – [in] pointer to matrix on the GPU
lda – [in] [int] specifies the leading dimension of A, lda >= rows
BP – [out] pointer to matrix on the host
ldb – [in] [int] specifies the leading dimension of B, ldb >= rows
stream – [in] specifies the stream into which this transfer request is queued

hipblasSetAtomicsMode#

hipblasStatus_t hipblasSetAtomicsMode(hipblasHandle_t handle, hipblasAtomicsMode_t atomics_mode)#: Set hipblasSetAtomicsMode.

hipblasGetAtomicsMode#

hipblasStatus_t hipblasGetAtomicsMode(hipblasHandle_t handle, hipblasAtomicsMode_t *atomics_mode)#: Get hipblasSetAtomicsMode.

hipblasStatusToString#

const char *hipblasStatusToString(hipblasStatus_t status)#

HIPBLAS Auxiliary API

hipblasStatusToString

Returns string representing hipblasStatus_t value

Parameters:: status – [in] [hipblasStatus_t] hipBLAS status to convert to string

Guidelines

Contents

Guidelines#

Naming conventions#

Notations#

hipBLAS Types#

Definitions#

hipblasHandle_t#

hipblasHalf#

hipblasInt8#

hipblasStride#

hipblasBfloat16#

hipblasComplex#

hipblasDoubleComplex#

Enums#

hipblasStatus_t#

hipblasOperation_t#

hipblasPointerMode_t#

hipblasFillMode_t#

hipblasDiagType_t#

hipblasSideMode_t#

hipblasDatatype_t#

hipblasGemmAlgo_t#

hipblasAtomicsMode_t#

hipBLAS Functions#

Level 1 BLAS#

Level 2 BLAS#

Level 3 BLAS#

SOLVER API#

BLAS Extensions#

Auxiliary#

hipblasCreate#

hipblasDestroy#

hipblasSetStream#

hipblasGetStream#

hipblasSetPointerMode#

hipblasGetPointerMode#

hipblasSetVector#

hipblasGetVector#

hipblasSetMatrix#

hipblasGetMatrix#

hipblasSetVectorAsync#

hipblasGetVectorAsync#

hipblasSetMatrixAsync#

hipblasGetMatrixAsync#

hipblasSetAtomicsMode#

hipblasGetAtomicsMode#

hipblasStatusToString#