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 unsigned 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
-
enumerator HIPBLAS_STATUS_SUCCESS#
hipblasOperation_t#
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.
-
enumerator HIPBLAS_POINTER_MODE_HOST#
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#
-
enumerator HIPBLAS_FILL_MODE_UPPER#
hipblasDiagType_t#
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#
-
enumerator HIPBLAS_SIDE_LEFT#
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
-
enumerator HIPBLAS_DATATYPE_INVALID#
Invalid datatype value, do not use
-
enumerator HIPBLAS_R_16F#
hipblasGemmAlgo_t#
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.
-
enumerator HIPBLAS_ATOMICS_NOT_ALLOWED#
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)#
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hipblasStatus_t hipblasDcopyBatched(hipblasHandle_t handle, int n, const double *const x[], int incx, double *const y[], int incy, int batchCount)#
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hipblasStatus_t hipblasCcopyBatched(hipblasHandle_t handle, int n, const hipblasComplex *const x[], int incx, hipblasComplex *const y[], int incy, int batchCount)#
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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
where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors.y_i := 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.
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
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hipblasStatus_t hipblasScopyStridedBatched(hipblasHandle_t handle, int n, const float *x, int incx, hipblasStride stridex, float *y, int incy, hipblasStride stridey, int batchCount)#
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hipblasStatus_t hipblasDcopyStridedBatched(hipblasHandle_t handle, int n, const double *x, int incx, hipblasStride stridex, double *y, int incy, hipblasStride stridey, int batchCount)#
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hipblasStatus_t hipblasCcopyStridedBatched(hipblasHandle_t handle, int n, const hipblasComplex *x, int incx, hipblasStride stridex, hipblasComplex *y, int incy, hipblasStride stridey, int batchCount)#
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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
where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors.y_i := 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.
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#
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hipblasStatus_t hipblasHdot(hipblasHandle_t handle, int n, const hipblasHalf *x, int incx, const hipblasHalf *y, int incy, hipblasHalf *result)#
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hipblasStatus_t hipblasBfdot(hipblasHandle_t handle, int n, const hipblasBfloat16 *x, int incx, const hipblasBfloat16 *y, int incy, hipblasBfloat16 *result)#
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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
dotc performs the dot product of the conjugate of complex vector x and complex vector yresult = x * 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.
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hipblasStatus_t hipblasHdotBatched(hipblasHandle_t handle, int n, const hipblasHalf *const x[], int incx, const hipblasHalf *const y[], int incy, int batchCount, hipblasHalf *result)#
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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)#
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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)#
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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
dotcBatched performs a batch of dot products of the conjugate of complex vector x and complex vector yresult_i = 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, …, batchCountresult_i = conjugate (x_i) * y_i;
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.
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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)#
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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)#
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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)#
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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)#
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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)#
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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)#
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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)#
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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
dotcStridedBatched performs a batch of dot products of the conjugate of complex vector x and complex vector yresult_i = 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, …, batchCountresult_i = conjugate (x_i) * y_i;
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#
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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.
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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#
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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)#
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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)#
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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)#
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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.
where (x_i) is the i-th instance of the batch.x_i := alpha * x_i
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.
where (x_i) is the i-th instance of the batch.x_i := alpha * x_i ,
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 *const x[], int incx, float *const y[], int incy, int batchCount)#
-
hipblasStatus_t hipblasDswapBatched(hipblasHandle_t handle, int n, double *const x[], int incx, double *const y[], int incy, int batchCount)#
-
hipblasStatus_t hipblasCswapBatched(hipblasHandle_t handle, int n, hipblasComplex *const x[], int incx, hipblasComplex *const y[], int incy, int batchCount)#
-
hipblasStatus_t hipblasZswapBatched(hipblasHandle_t handle, int n, hipblasDoubleComplex *const x[], int incx, hipblasDoubleComplex *const 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#
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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)#
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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)#
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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)#
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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
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.y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*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.
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.
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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)#
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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)#
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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)#
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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
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.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,
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.
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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)#
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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)#
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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)#
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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
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.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,
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#
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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)#
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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)#
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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)#
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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
where alpha and beta are scalars, x and y are vectors and A is an m by n matrix.y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*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.
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.
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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)#
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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)#
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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)#
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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
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.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,
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
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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)#
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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)#
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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)#
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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
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.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,
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#
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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)#
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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)#
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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)#
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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)#
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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)#
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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
where alpha is a scalar, x and y are vectors, and A is an m by n matrix.A := A + alpha*x*y**T , OR A := A + alpha*x*y**H for gerc
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.
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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)#
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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)#
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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)#
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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)#
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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)#
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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
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.A := A + alpha*x*y**T , OR A := A + alpha*x*y**H for gerc
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
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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)#
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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)#
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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)#
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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)#