hipblasgemmstridedbatchedex Interface Reference#
hipfort_hipblas::hipblasgemmstridedbatchedex Interface Reference
BLAS EX API. More...
Public Member Functions | |
| integer(kind(hipblas_status_success)) function | hipblasgemmstridedbatchedex_ (handle, transa, transb, m, n, k, alpha, a, atype, lda, stridea, b, btype, ldb, strideb, beta, c, ctype, ldc, stridec, batchcount, computetype, algo) |
Detailed Description
BLAS EX API.
The gemmStridedBatchedEx functions perform one of the strided_batched matrix-matrix operations:
C_i = alpha*op(A_i)*op(B_i) + beta*C_i, for i = 1, ..., batchCount
where op( X ) is one of:
op( X ) = X or op( X ) = X**T or op( X ) = X**H,
alpha and beta are scalars, and A, B, and C are strided_batched matrices, with op( A ) an m by k by batchCount strided_batched matrix, op( B ) a k by n by batchCount strided_batched matrix, and C an m by n by batchCount strided_batched matrix.
The strided_batched matrices are multiple matrices separated by a constant stride. The number of matrices is batchCount.
- Supported types are determined by the backend. See the rocBLAS or cuBLAS documentation.
hipblasGemmStridedBatchedExWithFlags is also available. This is identical to hipblasStridedBatchedGemmEx with the addition of a flags parameter which controls the flags used in Tensile to control gemm algorithms with the rocBLAS backend. When using a cuBLAS backend, this parameter is ignored.
- Parameters
-
[in] handle - [hipblasHandle_t] handle to the hipBLAS library context queue. [in] transA - [hipblasOperation_t] specifies the form of op( A ). [in] transB - [hipblasOperation_t] specifies the form of op( B ). [in] m - [int] matrix dimension m. [in] n - [int] matrix dimension n. [in] k - [int] matrix dimension k. [in] alpha - [const void *] device pointer or host pointer specifying the scalar alpha. Same datatype as computeType. [in] A - [void *] device pointer pointing to first matrix A_1. [in] aType [hipDataType] specifies the datatype of each matrix A_i. [in] lda - [int] specifies the leading dimension of each A_i. [in] strideA - [hipblasStride] specifies stride from start of one A_i matrix to the next A_(i + 1). [in] B - [void *] device pointer pointing to first matrix B_1. [in] bType [hipDataType] specifies the datatype of each matrix B_i. [in] ldb - [int] specifies the leading dimension of each B_i. [in] strideB - [hipblasStride] specifies stride from start of one B_i matrix to the next B_(i + 1). [in] beta - [const void *] device pointer or host pointer specifying the scalar beta. Same datatype as computeType. [in] C - [void *] device pointer pointing to first matrix C_1. [in] cType [hipDataType] specifies the datatype of each matrix C_i. [in] ldc - [int] specifies the leading dimension of each C_i. [in] strideC - [hipblasStride] specifies stride from start of one C_i matrix to the next C_(i + 1). [in] batchCount [int] number of gemm operations in the batch. [in] computeType [hipblasComputeType_t] specifies the datatype of computation. [in] algo - [hipblasGemmAlgo_t] enumerant specifying the algorithm type.
Member Function/Subroutine Documentation
◆ hipblasgemmstridedbatchedex_()
| integer(kind(hipblas_status_success)) function hipfort_hipblas::hipblasgemmstridedbatchedex::hipblasgemmstridedbatchedex_ | ( | type(c_ptr), value | handle, |
| integer(kind(hipblas_op_n)), value | transa, | ||
| integer(kind(hipblas_op_n)), value | transb, | ||
| integer(c_int), value | m, | ||
| integer(c_int), value | n, | ||
| integer(c_int), value | k, | ||
| type(c_ptr), value | alpha, | ||
| type(c_ptr), value | a, | ||
| integer(kind(hip_r_32f)), value | atype, | ||
| integer(c_int), value | lda, | ||
| integer(c_int64_t), value | stridea, | ||
| type(c_ptr), value | b, | ||
| integer(kind(hip_r_32f)), value | btype, | ||
| integer(c_int), value | ldb, | ||
| integer(c_int64_t), value | strideb, | ||
| type(c_ptr), value | beta, | ||
| type(c_ptr), value | c, | ||
| integer(kind(hip_r_32f)), value | ctype, | ||
| integer(c_int), value | ldc, | ||
| integer(c_int64_t), value | stridec, | ||
| integer(c_int), value | batchcount, | ||
| integer(kind(hipblas_compute_16f)), value | computetype, | ||
| integer(kind(hipblas_gemm_default)), value | algo | ||
| ) |
The documentation for this interface was generated from the following file: