/* ************************************************************************ * Copyright 2016-2019 Advanced Micro Devices, Inc. * ************************************************************************ */ #ifndef _ROCBLAS_FUNCTIONS_H_ #define _ROCBLAS_FUNCTIONS_H_ #include "rocblas-export.h" #include "rocblas-types.h" /*!\file * \brief rocblas_functions.h provides Basic Linear Algebra Subprograms of Level 1, 2 and 3, * using HIP optimized for AMD HCC-based GPU hardware. This library can also run on CUDA-based * NVIDIA GPUs. * This file exposes C89 BLAS interface */ /* * =========================================================================== * README: Please follow the naming convention * 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) * =========================================================================== */ /* ROCBLAS_VA_OPT_PRAGMA(pragma, ...) creates a _Pragma with stringized pragma if the trailing argument list is non-empty. __VA_OPT__ support is automatically detected if it's available; otherwise, the GCC/Clang ##__VA_ARGS__ extension is used to emulate it. */ #define ROCBLAS_VA_OPT_3RD_ARG(_1, _2, _3, ...) _3 #define ROCBLAS_VA_OPT_SUPPORTED(...) ROCBLAS_VA_OPT_3RD_ARG(__VA_OPT__(, ), 1, 0, ) #if ROCBLAS_VA_OPT_SUPPORTED(?) #define ROCBLAS_VA_OPT_PRAGMA(pragma, ...) __VA_OPT__(_Pragma(#pragma)) #else // ROCBLAS_VA_OPT_SUPPORTED #define ROCBLAS_VA_OPT_COUNT_IMPL(X, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N #define ROCBLAS_VA_OPT_COUNT(...) \ ROCBLAS_VA_OPT_COUNT_IMPL(, ##__VA_ARGS__, N, N, N, N, N, N, N, N, N, N, 0) #define ROCBLAS_VA_OPT_PRAGMA_SELECT0(...) #define ROCBLAS_VA_OPT_PRAGMA_SELECTN(pragma, ...) _Pragma(#pragma) #define ROCBLAS_VA_OPT_PRAGMA_IMPL2(pragma, count) ROCBLAS_VA_OPT_PRAGMA_SELECT##count(pragma) #define ROCBLAS_VA_OPT_PRAGMA_IMPL(pragma, count) ROCBLAS_VA_OPT_PRAGMA_IMPL2(pragma, count) #define ROCBLAS_VA_OPT_PRAGMA(pragma, ...) \ ROCBLAS_VA_OPT_PRAGMA_IMPL(pragma, ROCBLAS_VA_OPT_COUNT(__VA_ARGS__)) #endif // ROCBLAS_VA_OPT_SUPPORTED #ifdef __cplusplus extern "C" { #endif /* * =========================================================================== * level 1 BLAS * =========================================================================== */ /*! \brief BLAS Level 1 API \details scal scales each element of vector x with scalar alpha. x := alpha * x @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x. @param[in] alpha device pointer or host pointer for the scalar alpha. @param[inout] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sscal( rocblas_handle handle, rocblas_int n, const float* alpha, float* x, rocblas_int incx); ROCBLAS_EXPORT rocblas_status rocblas_dscal( rocblas_handle handle, rocblas_int n, const double* alpha, double* x, rocblas_int incx); ROCBLAS_EXPORT rocblas_status rocblas_cscal(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* alpha, rocblas_float_complex* x, rocblas_int incx); ROCBLAS_EXPORT rocblas_status rocblas_zscal(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* alpha, rocblas_double_complex* x, rocblas_int incx); ROCBLAS_EXPORT rocblas_status rocblas_csscal(rocblas_handle handle, rocblas_int n, const float* alpha, rocblas_float_complex* x, rocblas_int incx); ROCBLAS_EXPORT rocblas_status rocblas_zdscal(rocblas_handle handle, rocblas_int n, const double* alpha, rocblas_double_complex* x, rocblas_int incx); /*! \brief BLAS Level 1 API \details scal_batched scales each element of vector x_i with scalar alpha, for i = 1, … , batch_count. x_i := alpha * x_i where (x_i) is the i-th instance of the batch. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i. @param[in] alpha host pointer or device pointer for the scalar alpha. @param[inout] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. @param[in] batch_count [rocblas_int] specifies the number of batches in x. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sscal_batched(rocblas_handle handle, rocblas_int n, const float* alpha, float* const x[], rocblas_int incx, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dscal_batched(rocblas_handle handle, rocblas_int n, const double* alpha, double* const x[], rocblas_int incx, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_cscal_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* alpha, rocblas_float_complex* const x[], rocblas_int incx, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zscal_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* alpha, rocblas_double_complex* const x[], rocblas_int incx, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_csscal_batched(rocblas_handle handle, rocblas_int n, const float* alpha, rocblas_float_complex* const x[], rocblas_int incx, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zdscal_batched(rocblas_handle handle, rocblas_int n, const double* alpha, rocblas_double_complex* const x[], rocblas_int incx, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details scal_strided_batched scales each element of vector x_i with scalar alpha, for i = 1, … , batch_count. x_i := alpha * x_i , where (x_i) is the i-th instance of the batch. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i. @param[in] alpha host pointer or device pointer for the scalar alpha. @param[inout] x device pointer to the first vector (x_1) in the batch. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[in] stride_x [rocblas_stride] 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. @param[in] batch_count [rocblas_int] specifies the number of batches in x. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sscal_strided_batched(rocblas_handle handle, rocblas_int n, const float* alpha, float* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dscal_strided_batched(rocblas_handle handle, rocblas_int n, const double* alpha, double* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_cscal_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* alpha, rocblas_float_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zscal_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* alpha, rocblas_double_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_csscal_strided_batched(rocblas_handle handle, rocblas_int n, const float* alpha, rocblas_float_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zdscal_strided_batched(rocblas_handle handle, rocblas_int n, const double* alpha, rocblas_double_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details copy copies each element x[i] into y[i], for i = 1 , … , n y := x, @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x to be copied to y. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[out] y device pointer storing vector y. @param[in] incy [rocblas_int] specifies the increment for the elements of y. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_scopy(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, float* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_dcopy(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, double* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_ccopy(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_float_complex* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_zcopy(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_double_complex* y, rocblas_int incy); /*! \brief BLAS Level 1 API \details copy_batched copies each element x_i[j] into y_i[j], for j = 1 , … , n; i = 1 , … , batch_count y_i := x_i, where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i to be copied to y_i. @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each vector x_i. @param[out] y device array of device pointers storing each vector y_i. @param[in] incy [rocblas_int] specifies the increment for the elements of each vector y_i. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_scopy_batched(rocblas_handle handle, rocblas_int n, const float* const x[], rocblas_int incx, float* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dcopy_batched(rocblas_handle handle, rocblas_int n, const double* const x[], rocblas_int incx, double* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_ccopy_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* const x[], rocblas_int incx, rocblas_float_complex* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zcopy_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* const x[], rocblas_int incx, rocblas_double_complex* const y[], rocblas_int incy, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details copy_strided_batched copies each element x_i[j] into y_i[j], for j = 1 , … , n; i = 1 , … , batch_count y_i := x_i, where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i to be copied to y_i. @param[in] x device pointer to the first vector (x_1) in the batch. @param[in] incx [rocblas_int] specifies the increments for the elements of vectors x_i. @param[in] stridex [rocblas_stride] 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. @param[out] y device pointer to the first vector (y_1) in the batch. @param[in] incy [rocblas_int] specifies the increment for the elements of vectors y_i. @param[in] stridey [rocblas_stride] 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. @param[in] incy [rocblas_int] specifies the increment for the elements of y. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_scopy_strided_batched(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_stride stridex, float* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dcopy_strided_batched(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_stride stridex, double* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_ccopy_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_float_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zcopy_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_double_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details dot(u) performs the dot product of vectors x and y result = x * y; dotc performs the dot product of the conjugate of complex vector x and complex vector y result = conjugate (x) * y; @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x and y. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of y. @param[in] y device pointer storing vector y. @param[in] incy [rocblas_int] specifies the increment for the elements of y. @param[inout] result device pointer or host pointer to store the dot product. return is 0.0 if n <= 0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sdot(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, const float* y, rocblas_int incy, float* result); ROCBLAS_EXPORT rocblas_status rocblas_ddot(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, const double* y, rocblas_int incy, double* result); ROCBLAS_EXPORT rocblas_status rocblas_hdot(rocblas_handle handle, rocblas_int n, const rocblas_half* x, rocblas_int incx, const rocblas_half* y, rocblas_int incy, rocblas_half* result); ROCBLAS_EXPORT rocblas_status rocblas_bfdot(rocblas_handle handle, rocblas_int n, const rocblas_bfloat16* x, rocblas_int incx, const rocblas_bfloat16* y, rocblas_int incy, rocblas_bfloat16* result); ROCBLAS_EXPORT rocblas_status rocblas_cdotu(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, const rocblas_float_complex* y, rocblas_int incy, rocblas_float_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_zdotu(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, const rocblas_double_complex* y, rocblas_int incy, rocblas_double_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_cdotc(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, const rocblas_float_complex* y, rocblas_int incy, rocblas_float_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_zdotc(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, const rocblas_double_complex* y, rocblas_int incy, rocblas_double_complex* result); /*! \brief BLAS Level 1 API \details dot_batched(u) performs a batch of dot products of vectors x and y result_i = x_i * y_i; dotc_batched performs a batch of dot products of the conjugate of complex vector x and complex vector y result_i = conjugate (x_i) * y_i; where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i and y_i. @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. @param[in] y device array of device pointers storing each vector y_i. @param[in] incy [rocblas_int] specifies the increment for the elements of each y_i. @param[in] batch_count [rocblas_int] number of instances in the batch @param[inout] result device array or host array of batch_count size to store the dot products of each batch. return 0.0 for each element if n <= 0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sdot_batched(rocblas_handle handle, rocblas_int n, const float* const x[], rocblas_int incx, const float* const y[], rocblas_int incy, rocblas_int batch_count, float* result); ROCBLAS_EXPORT rocblas_status rocblas_ddot_batched(rocblas_handle handle, rocblas_int n, const double* const x[], rocblas_int incx, const double* const y[], rocblas_int incy, rocblas_int batch_count, double* result); ROCBLAS_EXPORT rocblas_status rocblas_hdot_batched(rocblas_handle handle, rocblas_int n, const rocblas_half* const x[], rocblas_int incx, const rocblas_half* const y[], rocblas_int incy, rocblas_int batch_count, rocblas_half* result); ROCBLAS_EXPORT rocblas_status rocblas_bfdot_batched(rocblas_handle handle, rocblas_int n, const rocblas_bfloat16* const x[], rocblas_int incx, const rocblas_bfloat16* const y[], rocblas_int incy, rocblas_int batch_count, rocblas_bfloat16* result); ROCBLAS_EXPORT rocblas_status rocblas_cdotu_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* const x[], rocblas_int incx, const rocblas_float_complex* const y[], rocblas_int incy, rocblas_int batch_count, rocblas_float_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_zdotu_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* const x[], rocblas_int incx, const rocblas_double_complex* const y[], rocblas_int incy, rocblas_int batch_count, rocblas_double_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_cdotc_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* const x[], rocblas_int incx, const rocblas_float_complex* const y[], rocblas_int incy, rocblas_int batch_count, rocblas_float_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_zdotc_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* const x[], rocblas_int incx, const rocblas_double_complex* const y[], rocblas_int incy, rocblas_int batch_count, rocblas_double_complex* result); /*! \brief BLAS Level 1 API \details dot_strided_batched(u) performs a batch of dot products of vectors x and y result_i = x_i * y_i; dotc_strided_batched performs a batch of dot products of the conjugate of complex vector x and complex vector y result_i = conjugate (x_i) * y_i; where (x_i, y_i) is the i-th instance of the batch. x_i and y_i are vectors, for i = 1, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i and y_i. @param[in] x device pointer to the first vector (x_1) in the batch. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. @param[in] stridex [rocblas_stride] stride from the start of one vector (x_i) and the next one (x_i+1) @param[in] y device pointer to the first vector (y_1) in the batch. @param[in] incy [rocblas_int] specifies the increment for the elements of each y_i. @param[in] stridey [rocblas_stride] stride from the start of one vector (y_i) and the next one (y_i+1) @param[in] batch_count [rocblas_int] number of instances in the batch @param[inout] result device array or host array of batch_count size to store the dot products of each batch. return 0.0 for each element if n <= 0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sdot_strided_batched(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_stride stridex, const float* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, float* result); ROCBLAS_EXPORT rocblas_status rocblas_ddot_strided_batched(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_stride stridex, const double* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, double* result); ROCBLAS_EXPORT rocblas_status rocblas_hdot_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_half* x, rocblas_int incx, rocblas_stride stridex, const rocblas_half* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, rocblas_half* result); ROCBLAS_EXPORT rocblas_status rocblas_bfdot_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_bfloat16* x, rocblas_int incx, rocblas_stride stridex, const rocblas_bfloat16* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, rocblas_bfloat16* result); ROCBLAS_EXPORT rocblas_status rocblas_cdotu_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, const rocblas_float_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, rocblas_float_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_zdotu_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, const rocblas_double_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, rocblas_double_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_cdotc_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, const rocblas_float_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, rocblas_float_complex* result); ROCBLAS_EXPORT rocblas_status rocblas_zdotc_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, const rocblas_double_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count, rocblas_double_complex* result); /*! \brief BLAS Level 1 API \details swap interchanges vectors x and y. y := x; x := y @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x and y. @param[inout] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[inout] y device pointer storing vector y. @param[in] incy [rocblas_int] specifies the increment for the elements of y. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sswap( rocblas_handle handle, rocblas_int n, float* x, rocblas_int incx, float* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_dswap( rocblas_handle handle, rocblas_int n, double* x, rocblas_int incx, double* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_cswap(rocblas_handle handle, rocblas_int n, rocblas_float_complex* x, rocblas_int incx, rocblas_float_complex* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_zswap(rocblas_handle handle, rocblas_int n, rocblas_double_complex* x, rocblas_int incx, rocblas_double_complex* y, rocblas_int incy); /*! \brief BLAS Level 1 API \details swap_batched interchanges vectors x_i and y_i, for i = 1 , ... , batch_count y_i := x_i; x_i := y_i @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i and y_i. @param[inout] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. @param[inout] y device array of device pointers storing each vector y_i. @param[in] incy [rocblas_int] specifies the increment for the elements of each y_i. @param[in] batch_count [rocblas_int] number of instances in the batch. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sswap_batched(rocblas_handle handle, rocblas_int n, float* x[], rocblas_int incx, float* y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dswap_batched(rocblas_handle handle, rocblas_int n, double* x[], rocblas_int incx, double* y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_cswap_batched(rocblas_handle handle, rocblas_int n, rocblas_float_complex* x[], rocblas_int incx, rocblas_float_complex* y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zswap_batched(rocblas_handle handle, rocblas_int n, rocblas_double_complex* x[], rocblas_int incx, rocblas_double_complex* y[], rocblas_int incy, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details swap_strided_batched interchanges vectors x_i and y_i, for i = 1 , ... , batch_count y_i := x_i; x_i := y_i @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in each x_i and y_i. @param[inout] x device pointer to the first vector x_1. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[in] stridex [rocblas_stride] 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. @param[inout] y device pointer to the first vector y_1. @param[in] incy [rocblas_int] specifies the increment for the elements of y. @param[in] stridey [rocblas_stride] 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. @param[in] batch_count [rocblas_int] number of instances in the batch. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sswap_strided_batched(rocblas_handle handle, rocblas_int n, float* x, rocblas_int incx, rocblas_stride stridex, float* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dswap_strided_batched(rocblas_handle handle, rocblas_int n, double* x, rocblas_int incx, rocblas_stride stridex, double* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_cswap_strided_batched(rocblas_handle handle, rocblas_int n, rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_float_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zswap_strided_batched(rocblas_handle handle, rocblas_int n, rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_double_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details axpy computes constant alpha multiplied by vector x, plus vector y y := alpha * x + y @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x and y. @param[in] alpha device pointer or host pointer to specify the scalar alpha. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[out] y device pointer storing vector y. @param[inout] incy [rocblas_int] specifies the increment for the elements of y. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_saxpy(rocblas_handle handle, rocblas_int n, const float* alpha, const float* x, rocblas_int incx, float* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_daxpy(rocblas_handle handle, rocblas_int n, const double* alpha, const double* x, rocblas_int incx, double* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_haxpy(rocblas_handle handle, rocblas_int n, const rocblas_half* alpha, const rocblas_half* x, rocblas_int incx, rocblas_half* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_caxpy(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* alpha, const rocblas_float_complex* x, rocblas_int incx, rocblas_float_complex* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_zaxpy(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* alpha, const rocblas_double_complex* x, rocblas_int incx, rocblas_double_complex* y, rocblas_int incy); /*! \brief BLAS Level 1 API \details axpy_batched compute y := alpha * x + y over a set of batched vectors. @param[in] handle rocblas_handle handle to the rocblas library context queue. @param[in] n rocblas_int @param[in] alpha specifies the scalar alpha. @param[in] x pointer storing vector x on the GPU. @param[in] incx rocblas_int specifies the increment for the elements of x. @param[out] y pointer storing vector y on the GPU. @param[inout] incy rocblas_int specifies the increment for the elements of y. @param[in] batch_count rocblas_int number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_haxpy_batched(rocblas_handle handle, rocblas_int n, const rocblas_half* alpha, const rocblas_half* const x[], rocblas_int incx, rocblas_half* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_saxpy_batched(rocblas_handle handle, rocblas_int n, const float* alpha, const float* const x[], rocblas_int incx, float* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_daxpy_batched(rocblas_handle handle, rocblas_int n, const double* alpha, const double* const x[], rocblas_int incx, double* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_caxpy_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* alpha, const rocblas_float_complex* const x[], rocblas_int incx, rocblas_float_complex* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zaxpy_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* alpha, const rocblas_double_complex* const x[], rocblas_int incx, rocblas_double_complex* const y[], rocblas_int incy, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details axpy_batched compute y := alpha * x + y over a set of strided batched vectors. @param[in] handle rocblas_handle handle to the rocblas library context queue. @param[in] n rocblas_int @param[in] alpha specifies the scalar alpha. @param[in] x pointer storing vector x on the GPU. @param[in] incx rocblas_int specifies the increment for the elements of x. @param[in] stridex rocblas_stride specifies the increment between vectors of x. @param[out] y pointer storing vector y on the GPU. @param[inout] incy rocblas_int specifies the increment for the elements of y. @param[in] stridey rocblas_stride specifies the increment between vectors of y. @param[in] batch_count rocblas_int number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_haxpy_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_half* alpha, const rocblas_half* x, rocblas_int incx, rocblas_stride stridex, rocblas_half* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_saxpy_strided_batched(rocblas_handle handle, rocblas_int n, const float* alpha, const float* x, rocblas_int incx, rocblas_stride stridex, float* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_daxpy_strided_batched(rocblas_handle handle, rocblas_int n, const double* alpha, const double* x, rocblas_int incx, rocblas_stride stridex, double* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_caxpy_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* alpha, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_float_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zaxpy_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* alpha, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_double_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details 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 @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x and y. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. incx must be > 0. @param[inout] result device pointer or host pointer to store the asum product. return is 0.0 if n <= 0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sasum( rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, float* result); ROCBLAS_EXPORT rocblas_status rocblas_dasum( rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, double* result); ROCBLAS_EXPORT rocblas_status rocblas_scasum(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, float* result); ROCBLAS_EXPORT rocblas_status rocblas_dzasum(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, double* result); /*! \brief BLAS Level 1 API \details asum_batched 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, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each vector x_i @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[out] results device array or host array of batch_count size for results. return is 0.0 if n, incx<=0. @param[in] batch_count [rocblas_int] number of instances in the batch. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sasum_batched(rocblas_handle handle, rocblas_int n, const float* const x[], rocblas_int incx, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dasum_batched(rocblas_handle handle, rocblas_int n, const double* const x[], rocblas_int incx, rocblas_int batch_count, double* results); ROCBLAS_EXPORT rocblas_status rocblas_scasum_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* const x[], rocblas_int incx, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dzasum_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* const x[], rocblas_int incx, rocblas_int batch_count, double* results); /*! \brief BLAS Level 1 API \details asum_strided_batched 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, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each vector x_i @param[in] x device pointer to the first vector x_1. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[in] stridex [rocblas_stride] 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. @param[out] results device pointer or host pointer to array for storing contiguous batch_count results. return is 0.0 if n, incx<=0. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sasum_strided_batched(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dasum_strided_batched(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, double* results); ROCBLAS_EXPORT rocblas_status rocblas_scasum_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dzasum_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, double* results); /*! \brief BLAS Level 1 API \details 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 @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of y. @param[inout] result device pointer or host pointer to store the nrm2 product. return is 0.0 if n, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_snrm2( rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, float* result); ROCBLAS_EXPORT rocblas_status rocblas_dnrm2( rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, double* result); ROCBLAS_EXPORT rocblas_status rocblas_scnrm2(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, float* result); ROCBLAS_EXPORT rocblas_status rocblas_dznrm2(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, double* result); /*! \brief BLAS Level 1 API \details nrm2_batched 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, ..., batch_count result := sqrt( x_i**H*x_i ) for complex vectors x, for i = 1, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each x_i. @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[in] batch_count [rocblas_int] number of instances in the batch @param[out] results device pointer or host pointer to array of batch_count size for nrm2 results. return is 0.0 for each element if n <= 0, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_snrm2_batched(rocblas_handle handle, rocblas_int n, const float* const x[], rocblas_int incx, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dnrm2_batched(rocblas_handle handle, rocblas_int n, const double* const x[], rocblas_int incx, rocblas_int batch_count, double* results); ROCBLAS_EXPORT rocblas_status rocblas_scnrm2_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* const x[], rocblas_int incx, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dznrm2_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* const x[], rocblas_int incx, rocblas_int batch_count, double* results); /*! \brief BLAS Level 1 API \details nrm2_strided_batched computes the euclidean norm over a batch of real or complex vectors := sqrt( x_i'*x_i ) for real vectors x, for i = 1, ..., batch_count := sqrt( x_i**H*x_i ) for complex vectors, for i = 1, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each x_i. @param[in] x device pointer to the first vector x_1. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[in] stridex [rocblas_stride] 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. @param[in] batch_count [rocblas_int] number of instances in the batch @param[out] results device pointer or host pointer to array for storing contiguous batch_count results. return is 0.0 for each element if n <= 0, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_snrm2_strided_batched(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dnrm2_strided_batched(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, double* results); ROCBLAS_EXPORT rocblas_status rocblas_scnrm2_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, float* results); ROCBLAS_EXPORT rocblas_status rocblas_dznrm2_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, double* results); /*! \brief BLAS Level 1 API \details amax finds the first index of the element of maximum magnitude of a vector x. vector @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of y. @param[inout] result device pointer or host pointer to store the amax index. return is 0.0 if n, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_isamax( rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_idamax( rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_icamax(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_izamax(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_int* result); /*! \brief BLAS Level 1 API \details amax_batched finds the first index of the element of maximum magnitude of each vector x_i in a batch, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each vector x_i @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[in] batch_count [rocblas_int] number of instances in the batch, must be > 0. @param[out] result device or host array of pointers of batch_count size for results. return is 0 if n, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_isamax_batched(rocblas_handle handle, rocblas_int n, const float* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_idamax_batched(rocblas_handle handle, rocblas_int n, const double* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_icamax_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_izamax_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); /*! \brief BLAS Level 1 API \details amax_strided_batched finds the first index of the element of maximum magnitude of each vector x_i in a batch, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each vector x_i @param[in] x device pointer to the first vector x_1. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[in] stridex [rocblas_stride] specifies the pointer increment between one x_i and the next x_(i + 1). @param[in] batch_count [rocblas_int] number of instances in the batch @param[out] result device or host pointer for storing contiguous batch_count results. return is 0 if n <= 0, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_isamax_strided_batched(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_idamax_strided_batched(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_icamax_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_izamax_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); /*! \brief BLAS Level 1 API \details amin finds the first index of the element of minimum magnitude of a vector x. vector @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] the number of elements in x. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of y. @param[inout] result device pointer or host pointer to store the amin index. return is 0.0 if n, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_isamin( rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_idamin( rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_icamin(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_izamin(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_int* result); /*! \brief BLAS Level 1 API \details amin_batched finds the first index of the element of minimum magnitude of each vector x_i in a batch, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each vector x_i @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[in] batch_count [rocblas_int] number of instances in the batch, must be > 0. @param[out] result device or host pointers to array of batch_count size for results. return is 0 if n, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_isamin_batched(rocblas_handle handle, rocblas_int n, const float* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_idamin_batched(rocblas_handle handle, rocblas_int n, const double* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_icamin_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_izamin_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* const x[], rocblas_int incx, rocblas_int batch_count, rocblas_int* result); /*! \brief BLAS Level 1 API \details amin_strided_batched finds the first index of the element of minimum magnitude of each vector x_i in a batch, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each vector x_i @param[in] x device pointer to the first vector x_1. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. incx must be > 0. @param[in] stridex [rocblas_stride] specifies the pointer increment between one x_i and the next x_(i + 1) @param[in] batch_count [rocblas_int] number of instances in the batch @param[out] result device or host pointer to array for storing contiguous batch_count results. return is 0 if n <= 0, incx<=0. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_isamin_strided_batched(rocblas_handle handle, rocblas_int n, const float* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_idamin_strided_batched(rocblas_handle handle, rocblas_int n, const double* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_icamin_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); ROCBLAS_EXPORT rocblas_status rocblas_izamin_strided_batched(rocblas_handle handle, rocblas_int n, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, rocblas_int batch_count, rocblas_int* result); /*! \brief BLAS Level 1 API \details 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 rocblas_set_pointer_mode. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in the x and y vectors. @param[inout] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment between elements of x. @param[inout] y device pointer storing vector y. @param[in] incy [rocblas_int] specifies the increment between elements of y. @param[in] c device pointer or host pointer storing scalar cosine component of the rotation matrix. @param[in] s device pointer or host pointer storing scalar sine component of the rotation matrix. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srot(rocblas_handle handle, rocblas_int n, float* x, rocblas_int incx, float* y, rocblas_int incy, const float* c, const float* s); ROCBLAS_EXPORT rocblas_status rocblas_drot(rocblas_handle handle, rocblas_int n, double* x, rocblas_int incx, double* y, rocblas_int incy, const double* c, const double* s); ROCBLAS_EXPORT rocblas_status rocblas_crot(rocblas_handle handle, rocblas_int n, rocblas_float_complex* x, rocblas_int incx, rocblas_float_complex* y, rocblas_int incy, const float* c, const rocblas_float_complex* s); ROCBLAS_EXPORT rocblas_status rocblas_csrot(rocblas_handle handle, rocblas_int n, rocblas_float_complex* x, rocblas_int incx, rocblas_float_complex* y, rocblas_int incy, const float* c, const float* s); ROCBLAS_EXPORT rocblas_status rocblas_zrot(rocblas_handle handle, rocblas_int n, rocblas_double_complex* x, rocblas_int incx, rocblas_double_complex* y, rocblas_int incy, const double* c, const rocblas_double_complex* s); ROCBLAS_EXPORT rocblas_status rocblas_zdrot(rocblas_handle handle, rocblas_int n, rocblas_double_complex* x, rocblas_int incx, rocblas_double_complex* y, rocblas_int incy, const double* c, const double* s); /*! \brief BLAS Level 1 API \details rot_batched 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, ..., batch_count. Scalars c and s may be stored in either host or device memory, location is specified by calling rocblas_set_pointer_mode. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each x_i and y_i vectors. @param[inout] x device array of deivce pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment between elements of each x_i. @param[inout] y device array of device pointers storing each vector y_i. @param[in] incy [rocblas_int] specifies the increment between elements of each y_i. @param[in] c device pointer or host pointer to scalar cosine component of the rotation matrix. @param[in] s device pointer or host pointer to scalar sine component of the rotation matrix. @param[in] batch_count [rocblas_int] the number of x and y arrays, i.e. the number of batches. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srot_batched(rocblas_handle handle, rocblas_int n, float* const x[], rocblas_int incx, float* const y[], rocblas_int incy, const float* c, const float* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drot_batched(rocblas_handle handle, rocblas_int n, double* const x[], rocblas_int incx, double* const y[], rocblas_int incy, const double* c, const double* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_crot_batched(rocblas_handle handle, rocblas_int n, rocblas_float_complex* const x[], rocblas_int incx, rocblas_float_complex* const y[], rocblas_int incy, const float* c, const rocblas_float_complex* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_csrot_batched(rocblas_handle handle, rocblas_int n, rocblas_float_complex* const x[], rocblas_int incx, rocblas_float_complex* const y[], rocblas_int incy, const float* c, const float* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zrot_batched(rocblas_handle handle, rocblas_int n, rocblas_double_complex* const x[], rocblas_int incx, rocblas_double_complex* const y[], rocblas_int incy, const double* c, const rocblas_double_complex* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zdrot_batched(rocblas_handle handle, rocblas_int n, rocblas_double_complex* const x[], rocblas_int incx, rocblas_double_complex* const y[], rocblas_int incy, const double* c, const double* s, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details rot_strided_batched 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, ..., batch_count. Scalars c and s may be stored in either host or device memory, location is specified by calling rocblas_set_pointer_mode. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in each x_i and y_i vectors. @param[inout] x device pointer to the first vector x_1. @param[in] incx [rocblas_int] specifies the increment between elements of each x_i. @param[in] stride_x [rocblas_stride] specifies the increment from the beginning of x_i to the beginning of x_(i+1) @param[inout] y device pointer to the first vector y_1. @param[in] incy [rocblas_int] specifies the increment between elements of each y_i. @param[in] stride_y [rocblas_stride] specifies the increment from the beginning of y_i to the beginning of y_(i+1) @param[in] c device pointer or host pointer to scalar cosine component of the rotation matrix. @param[in] s device pointer or host pointer to scalar sine component of the rotation matrix. @param[in] batch_count [rocblas_int] the number of x and y arrays, i.e. the number of batches. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srot_strided_batched(rocblas_handle handle, rocblas_int n, float* x, rocblas_int incx, rocblas_stride stride_x, float* y, rocblas_int incy, rocblas_stride stride_y, const float* c, const float* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drot_strided_batched(rocblas_handle handle, rocblas_int n, double* x, rocblas_int incx, rocblas_stride stride_x, double* y, rocblas_int incy, rocblas_stride stride_y, const double* c, const double* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_crot_strided_batched(rocblas_handle handle, rocblas_int n, rocblas_float_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_float_complex* y, rocblas_int incy, rocblas_stride stride_y, const float* c, const rocblas_float_complex* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_csrot_strided_batched(rocblas_handle handle, rocblas_int n, rocblas_float_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_float_complex* y, rocblas_int incy, rocblas_stride stride_y, const float* c, const float* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zrot_strided_batched(rocblas_handle handle, rocblas_int n, rocblas_double_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_double_complex* y, rocblas_int incy, rocblas_stride stride_y, const double* c, const rocblas_double_complex* s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zdrot_strided_batched(rocblas_handle handle, rocblas_int n, rocblas_double_complex* x, rocblas_int incx, rocblas_stride stride_x, rocblas_double_complex* y, rocblas_int incy, rocblas_stride stride_y, const double* c, const double* s, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details 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 rocblas_set_pointer_mode. If the pointer mode is set to rocblas_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 rocblas_pointer_mode_device, this function returns immediately and synchronization is required to read the results. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[inout] a device pointer or host pointer to input vector element, overwritten with r. @param[inout] b device pointer or host pointer to input vector element, overwritten with z. @param[inout] c device pointer or host pointer to cosine element of Givens rotation. @param[inout] s device pointer or host pointer sine element of Givens rotation. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotg(rocblas_handle handle, float* a, float* b, float* c, float* s); ROCBLAS_EXPORT rocblas_status rocblas_drotg(rocblas_handle handle, double* a, double* b, double* c, double* s); ROCBLAS_EXPORT rocblas_status rocblas_crotg(rocblas_handle handle, rocblas_float_complex* a, rocblas_float_complex* b, float* c, rocblas_float_complex* s); ROCBLAS_EXPORT rocblas_status rocblas_zrotg(rocblas_handle handle, rocblas_double_complex* a, rocblas_double_complex* b, double* c, rocblas_double_complex* s); /*! \brief BLAS Level 1 API \details rotg_batched creates the Givens rotation matrix for the batched vectors (a_i b_i), for i = 1, ..., batch_count. a, b, c, and s may be stored in either host or device memory, location is specified by calling rocblas_set_pointer_mode. If the pointer mode is set to rocblas_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 rocblas_pointer_mode_device, this function returns immediately and synchronization is required to read the results. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[inout] a device array of device pointers storing each single input vector element a_i, overwritten with r_i. @param[inout] b device array of device pointers storing each single input vector element b_i, overwritten with z_i. @param[inout] c device array of device pointers storing each cosine element of Givens rotation for the batch. @param[inout] s device array of device pointers storing each sine element of Givens rotation for the batch. @param[in] batch_count [rocblas_int] number of batches (length of arrays a, b, c, and s). ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotg_batched(rocblas_handle handle, float* const a[], float* const b[], float* const c[], float* const s[], rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drotg_batched(rocblas_handle handle, double* const a[], double* const b[], double* const c[], double* const s[], rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_crotg_batched(rocblas_handle handle, rocblas_float_complex* const a[], rocblas_float_complex* const b[], float* const c[], rocblas_float_complex* const s[], rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zrotg_batched(rocblas_handle handle, rocblas_double_complex* const a[], rocblas_double_complex* const b[], double* const c[], rocblas_double_complex* const s[], rocblas_int batch_count); /*! \brief BLAS Level 1 API \details rotg_strided_batched creates the Givens rotation matrix for the strided batched vectors (a_i b_i), for i = 1, ..., batch_count. a, b, c, and s may be stored in either host or device memory, location is specified by calling rocblas_set_pointer_mode. If the pointer mode is set to rocblas_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 rocblas_pointer_mode_device, this function returns immediately and synchronization is required to read the results. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[inout] a device strided_batched pointer or host strided_batched pointer to first single input vector element a_1, overwritten with r. @param[in] stride_a [rocblas_stride] distance between elements of a in batch (distance between a_i and a_(i + 1)) @param[inout] b device strided_batched pointer or host strided_batched pointer to first single input vector element b_1, overwritten with z. @param[in] stride_b [rocblas_stride] distance between elements of b in batch (distance between b_i and b_(i + 1)) @param[inout] c device strided_batched pointer or host strided_batched pointer to first cosine element of Givens rotations c_1. @param[in] stride_c [rocblas_stride] distance between elements of c in batch (distance between c_i and c_(i + 1)) @param[inout] s device strided_batched pointer or host strided_batched pointer to sine element of Givens rotations s_1. @param[in] stride_s [rocblas_stride] distance between elements of s in batch (distance between s_i and s_(i + 1)) @param[in] batch_count [rocblas_int] number of batches (length of arrays a, b, c, and s). ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotg_strided_batched(rocblas_handle handle, float* a, rocblas_stride stride_a, float* b, rocblas_stride stride_b, float* c, rocblas_stride stride_c, float* s, rocblas_stride stride_s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drotg_strided_batched(rocblas_handle handle, double* a, rocblas_stride stride_a, double* b, rocblas_stride stride_b, double* c, rocblas_stride stride_c, double* s, rocblas_stride stride_s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_crotg_strided_batched(rocblas_handle handle, rocblas_float_complex* a, rocblas_stride stride_a, rocblas_float_complex* b, rocblas_stride stride_b, float* c, rocblas_stride stride_c, rocblas_float_complex* s, rocblas_stride stride_s, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zrotg_strided_batched(rocblas_handle handle, rocblas_double_complex* a, rocblas_stride stride_a, rocblas_double_complex* b, rocblas_stride stride_b, double* c, rocblas_stride stride_c, rocblas_double_complex* s, rocblas_stride stride_s, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details rotm applies the modified Givens rotation matrix defined by param to vectors x and y. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in the x and y vectors. @param[inout] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment between elements of x. @param[inout] y device pointer storing vector y. @param[in] incy [rocblas_int] specifies the increment between elements of y. @param[in] param 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 rocblas_set_pointer_mode. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotm(rocblas_handle handle, rocblas_int n, float* x, rocblas_int incx, float* y, rocblas_int incy, const float* param); ROCBLAS_EXPORT rocblas_status rocblas_drotm(rocblas_handle handle, rocblas_int n, double* x, rocblas_int incx, double* y, rocblas_int incy, const double* param); /*! \brief BLAS Level 1 API \details rotm_batched applies the modified Givens rotation matrix defined by param_i to batched vectors x_i and y_i, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in the x and y vectors. @param[inout] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment between elements of each x_i. @param[inout] y device array of device pointers storing each vector y_1. @param[in] incy [rocblas_int] specifies the increment between elements of each y_i. @param[in] param 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. @param[in] batch_count [rocblas_int] the number of x and y arrays, i.e. the number of batches. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotm_batched(rocblas_handle handle, rocblas_int n, float* const x[], rocblas_int incx, float* const y[], rocblas_int incy, const float* const param[], rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drotm_batched(rocblas_handle handle, rocblas_int n, double* const x[], rocblas_int incx, double* const y[], rocblas_int incy, const double* const param[], rocblas_int batch_count); /*! \brief BLAS Level 1 API \details rotm_strided_batched applies the modified Givens rotation matrix defined by param_i to strided batched vectors x_i and y_i, for i = 1, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] n [rocblas_int] number of elements in the x and y vectors. @param[inout] x device pointer pointing to first strided batched vector x_1. @param[in] incx [rocblas_int] specifies the increment between elements of each x_i. @param[in] stride_x [rocblas_stride] specifies the increment between the beginning of x_i and x_(i + 1) @param[inout] y device pointer pointing to first strided batched vector y_1. @param[in] incy [rocblas_int] specifies the increment between elements of each y_i. @param[in] stride_y [rocblas_stride] specifies the increment between the beginning of y_i and y_(i + 1) @param[in] param 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. @param[in] stride_param [rocblas_stride] specifies the increment between the beginning of param_i and param_(i + 1) @param[in] batch_count [rocblas_int] the number of x and y arrays, i.e. the number of batches. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotm_strided_batched(rocblas_handle handle, rocblas_int n, float* x, rocblas_int incx, rocblas_stride stride_x, float* y, rocblas_int incy, rocblas_stride stride_y, const float* param, rocblas_stride stride_param, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drotm_strided_batched(rocblas_handle handle, rocblas_int n, double* x, rocblas_int incx, rocblas_stride stride_x, double* y, rocblas_int incy, rocblas_stride stride_y, const double* param, rocblas_stride stride_param, rocblas_int batch_count); /*! \brief BLAS Level 1 API \details 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 rocblas_set_pointer_mode. If the pointer mode is set to rocblas_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 rocblas_pointer_mode_device, this function returns immediately and synchronization is required to read the results. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[inout] d1 device pointer or host pointer to input scalar that is overwritten. @param[inout] d2 device pointer or host pointer to input scalar that is overwritten. @param[inout] x1 device pointer or host pointer to input scalar that is overwritten. @param[in] y1 device pointer or host pointer to input scalar. @param[out] param 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 rocblas_set_pointer_mode. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotmg( rocblas_handle handle, float* d1, float* d2, float* x1, const float* y1, float* param); ROCBLAS_EXPORT rocblas_status rocblas_drotmg( rocblas_handle handle, double* d1, double* d2, double* x1, const double* y1, double* param); /*! \brief BLAS Level 1 API \details rotmg_batched creates the modified Givens rotation matrix for the batched vectors (d1_i * x1_i, d2_i * y1_i), for i = 1, ..., batch_count. Parameters may be stored in either host or device memory, location is specified by calling rocblas_set_pointer_mode. If the pointer mode is set to rocblas_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 rocblas_pointer_mode_device, this function returns immediately and synchronization is required to read the results. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[inout] d1 device batched array or host batched array of input scalars that is overwritten. @param[inout] d2 device batched array or host batched array of input scalars that is overwritten. @param[inout] x1 device batched array or host batched array of input scalars that is overwritten. @param[in] y1 device batched array or host batched array of input scalars. @param[out] param 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 rocblas_set_pointer_mode. @param[in] batch_count [rocblas_int] the number of instances in the batch. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotmg_batched(rocblas_handle handle, float* const d1[], float* const d2[], float* const x1[], const float* const y1[], float* const param[], rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drotmg_batched(rocblas_handle handle, double* const d1[], double* const d2[], double* const x1[], const double* const y1[], double* const param[], rocblas_int batch_count); /*! \brief BLAS Level 1 API \details rotmg_strided_batched creates the modified Givens rotation matrix for the strided batched vectors (d1_i * x1_i, d2_i * y1_i), for i = 1, ..., batch_count. Parameters may be stored in either host or device memory, location is specified by calling rocblas_set_pointer_mode. If the pointer mode is set to rocblas_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 rocblas_pointer_mode_device, this function returns immediately and synchronization is required to read the results. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[inout] d1 device strided_batched array or host strided_batched array of input scalars that is overwritten. @param[in] stride_d1 [rocblas_stride] specifies the increment between the beginning of d1_i and d1_(i+1) @param[inout] d2 device strided_batched array or host strided_batched array of input scalars that is overwritten. @param[in] stride_d2 [rocblas_stride] specifies the increment between the beginning of d2_i and d2_(i+1) @param[inout] x1 device strided_batched array or host strided_batched array of input scalars that is overwritten. @param[in] stride_x1 [rocblas_stride] specifies the increment between the beginning of x1_i and x1_(i+1) @param[in] y1 device strided_batched array or host strided_batched array of input scalars. @param[in] stride_y1 [rocblas_stride] specifies the increment between the beginning of y1_i and y1_(i+1) @param[out] param device strided_batched array or host strided_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 rocblas_set_pointer_mode. @param[in] stride_param [rocblas_stride] specifies the increment between the beginning of param_i and param_(i + 1) @param[in] batch_count [rocblas_int] the number of instances in the batch. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_srotmg_strided_batched(rocblas_handle handle, float* d1, rocblas_stride stride_d1, float* d2, rocblas_stride stride_d2, float* x1, rocblas_stride stride_x1, const float* y1, rocblas_stride stride_y1, float* param, rocblas_stride stride_param, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_drotmg_strided_batched(rocblas_handle handle, double* d1, rocblas_stride stride_d1, double* d2, rocblas_stride stride_d2, double* x1, rocblas_stride stride_x1, const double* y1, rocblas_stride stride_y1, double* param, rocblas_stride stride_param, rocblas_int batch_count); /* * =========================================================================== * level 2 BLAS * =========================================================================== */ /*! \brief BLAS Level 2 API \details xGEMV performs one of the matrix-vector operations y := alpha*A*x + beta*y, or y := alpha*A**T*x + beta*y, or y := alpha*A**H*x + beta*y, where alpha and beta are scalars, x and y are vectors and A is an m by n matrix. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] trans [rocblas_operation] indicates whether matrix A is tranposed (conjugated) or not @param[in] m [rocblas_int] number of rows of matrix A @param[in] n [rocblas_int] number of columns of matrix A @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] A device pointer storing matrix A. @param[in] lda [rocblas_int] specifies the leading dimension of A. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[in] beta device pointer or host pointer to scalar beta. @param[inout] y device pointer storing vector y. @param[in] incy [rocblas_int] specifies the increment for the elements of y. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sgemv(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const float* alpha, const float* A, rocblas_int lda, const float* x, rocblas_int incx, const float* beta, float* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_dgemv(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const double* alpha, const double* A, rocblas_int lda, const double* x, rocblas_int incx, const double* beta, double* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_cgemv(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const rocblas_float_complex* alpha, const rocblas_float_complex* A, rocblas_int lda, const rocblas_float_complex* x, rocblas_int incx, const rocblas_float_complex* beta, rocblas_float_complex* y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_zgemv(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const rocblas_double_complex* alpha, const rocblas_double_complex* A, rocblas_int lda, const rocblas_double_complex* x, rocblas_int incx, const rocblas_double_complex* beta, rocblas_double_complex* y, rocblas_int incy); /*! \brief BLAS Level 2 API \details xGEMV_BATCHED performs a batch of matrix-vector operations y_i := alpha*A_i*x_i + beta*y_i, or y_i := alpha*A_i**T*x_i + beta*y_i, or y_i := alpha*A_i**H*x_i + beta*y_i, where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an m by n matrix, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] trans [rocblas_operation] indicates whether matrices A_i are tranposed (conjugated) or not @param[in] m [rocblas_int] number of rows of each matrix A_i @param[in] n [rocblas_int] number of columns of each matrix A_i @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] A device array of device pointers storing each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each matrix A_i. @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each vector x_i. @param[in] beta device pointer or host pointer to scalar beta. @param[inout] y device array of device pointers storing each vector y_i. @param[in] incy [rocblas_int] specifies the increment for the elements of each vector y_i. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sgemv_batched(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const float* alpha, const float* const A[], rocblas_int lda, const float* const x[], rocblas_int incx, const float* beta, float* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dgemv_batched(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const double* alpha, const double* const A[], rocblas_int lda, const double* const x[], rocblas_int incx, const double* beta, double* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_cgemv_batched(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const rocblas_float_complex* alpha, const rocblas_float_complex* const A[], rocblas_int lda, const rocblas_float_complex* const x[], rocblas_int incx, const rocblas_float_complex* beta, rocblas_float_complex* const y[], rocblas_int incy, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zgemv_batched(rocblas_handle handle, rocblas_operation trans, rocblas_int m, rocblas_int n, const rocblas_double_complex* alpha, const rocblas_double_complex* const A[], rocblas_int lda, const rocblas_double_complex* const x[], rocblas_int incx, const rocblas_double_complex* beta, rocblas_double_complex* const y[], rocblas_int incy, rocblas_int batch_count); /*! \brief BLAS Level 2 API \details xGEMV_STRIDED_BATCHED performs a batch of matrix-vector operations y_i := alpha*A_i*x_i + beta*y_i, or y_i := alpha*A_i**T*x_i + beta*y_i, or y_i := alpha*A_i**H*x_i + beta*y_i, where (A_i, x_i, y_i) is the i-th instance of the batch. alpha and beta are scalars, x_i and y_i are vectors and A_i is an m by n matrix, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] transA [rocblas_operation] indicates whether matrices A_i are tranposed (conjugated) or not @param[in] m [rocblas_int] number of rows of matrices A_i @param[in] n [rocblas_int] number of columns of matrices A_i @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] A device pointer to the first matrix (A_1) in the batch. @param[in] lda [rocblas_int] specifies the leading dimension of matrices A_i. @param[in] strideA [rocblas_stride] stride from the start of one matrix (A_i) and the next one (A_i+1) @param[in] x device pointer to the first vector (x_1) in the batch. @param[in] incx [rocblas_int] specifies the increment for the elements of vectors x_i. @param[in] stridex [rocblas_stride] 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. When trans equals rocblas_operation_none this typically means stride_x >= n * incx, otherwise stride_x >= m * incx. @param[in] beta device pointer or host pointer to scalar beta. @param[inout] y device pointer to the first vector (y_1) in the batch. @param[in] incy [rocblas_int] specifies the increment for the elements of vectors y_i. @param[in] stridey [rocblas_stride] 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. When trans equals rocblas_operation_none this typically means stride_y >= m * incy, otherwise stride_y >= n * incy. stridey should be non zero. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sgemv_strided_batched(rocblas_handle handle, rocblas_operation transA, rocblas_int m, rocblas_int n, const float* alpha, const float* A, rocblas_int lda, rocblas_stride strideA, const float* x, rocblas_int incx, rocblas_stride stridex, const float* beta, float* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dgemv_strided_batched(rocblas_handle handle, rocblas_operation transA, rocblas_int m, rocblas_int n, const double* alpha, const double* A, rocblas_int lda, rocblas_stride strideA, const double* x, rocblas_int incx, rocblas_stride stridex, const double* beta, double* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_cgemv_strided_batched(rocblas_handle handle, rocblas_operation transA, rocblas_int m, rocblas_int n, const rocblas_float_complex* alpha, const rocblas_float_complex* A, rocblas_int lda, rocblas_stride strideA, const rocblas_float_complex* x, rocblas_int incx, rocblas_stride stridex, const rocblas_float_complex* beta, rocblas_float_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zgemv_strided_batched(rocblas_handle handle, rocblas_operation transA, rocblas_int m, rocblas_int n, const rocblas_double_complex* alpha, const rocblas_double_complex* A, rocblas_int lda, rocblas_stride strideA, const rocblas_double_complex* x, rocblas_int incx, rocblas_stride stridex, const rocblas_double_complex* beta, rocblas_double_complex* y, rocblas_int incy, rocblas_stride stridey, rocblas_int batch_count); /*! \brief BLAS Level 2 API \details trsv solves A*x = b or A**T*x = b, where x and b are vectors and A is a triangular matrix. The vector x is overwritten on b. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] rocblas_fill_upper: A is an upper triangular matrix. rocblas_fill_lower: A is a lower triangular matrix. @param[in] transA [rocblas_operation] @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: A is assumed to be unit triangular. rocblas_diagonal_non_unit: A is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of b. m >= 0. @param[in] A device pointer storing matrix A, of dimension ( lda, m ) @param[in] lda [rocblas_int] specifies the leading dimension of A. lda = max( 1, m ). @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strsv(rocblas_handle handle, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, const float* A, rocblas_int lda, float* x, rocblas_int incx); ROCBLAS_EXPORT rocblas_status rocblas_dtrsv(rocblas_handle handle, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, const double* A, rocblas_int lda, double* x, rocblas_int incx); /*! \brief BLAS Level 2 API \details trsv_batched solves A_i*x_i = b_i or A_i**T*x_i = b_i, where (A_i, x_i, b_i) is the i-th instance of the batch. x_i and b_i are vectors and A_i is an m by m triangular matrix. The vector x is overwritten on b. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] rocblas_fill_upper: A is an upper triangular matrix. rocblas_fill_lower: A is a lower triangular matrix. @param[in] transA [rocblas_operation] @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: A is assumed to be unit triangular. rocblas_diagonal_non_unit: A is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of b. m >= 0. @param[in] A device array of device pointers storing each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. lda = max(1, m) @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strsv_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, const float* const A[], rocblas_int lda, float* const x[], rocblas_int incx, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dtrsv_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, const double* const A[], rocblas_int lda, double* const x[], rocblas_int incx, rocblas_int batch_count); /*! \brief BLAS Level 2 API \details trsv_strided_batched solves A_i*x_i = b_i or A_i**T*x_i = b_i, where (A_i, x_i, b_i) is the i-th instance of the batch. x_i and b_i are vectors and A_i is an m by m triangular matrix, for i = 1, ..., batch_count. The vector x is overwritten on b. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] rocblas_fill_upper: A is an upper triangular matrix. rocblas_fill_lower: A is a lower triangular matrix. @param[in] transA [rocblas_operation] @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: A is assumed to be unit triangular. rocblas_diagonal_non_unit: A is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of each b_i. m >= 0. @param[in] A device pointer to the first matrix (A_1) in the batch, of dimension ( lda, m ) @param[in] stride_A [rocblas_stride] stride from the start of one A_i matrix to the next A_(i + 1) @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. lda = max( 1, m ). @param[in, out] x device pointer to the first vector (x_1) in the batch. @param[in] stride_x [rocblas_stride] stride from the start of one x_i vector to the next x_(i + 1) @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strsv_strided_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, const float* A, rocblas_int lda, rocblas_stride stride_A, float* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dtrsv_strided_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, const double* A, rocblas_int lda, rocblas_stride stride_A, double* x, rocblas_int incx, rocblas_stride stride_x, rocblas_int batch_count); // TODO: Add ! for doc. /* \brief BLAS Level 2 API \details xHE(SY)MV performs the matrix-vector operation: y := alpha*A*x + beta*y, where alpha and beta are scalars, x and y are n element vectors and A is an n by n Hermitian(Symmetric) matrix. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] specifies whether the upper or lower @param[in] n [rocblas_int] @param[in] alpha specifies the scalar alpha. @param[in] A pointer storing matrix A on the GPU. @param[in] lda [rocblas_int] specifies the leading dimension of A. @param[in] x pointer storing vector x on the GPU. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[in] beta specifies the scalar beta. @param[out] y pointer storing vector y on the GPU. @param[in] incy [rocblas_int] specifies the increment for the elements of y. ********************************************************************/ /* not implemented ROCBLAS_EXPORT rocblas_status rocblas_ssymv(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const float *alpha, const float *A, rocblas_int lda, const float *x, rocblas_int incx, const float *beta, float *y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_dsymv(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const double *alpha, const double *A, rocblas_int lda, const double *x, rocblas_int incx, const double *beta, double *y, rocblas_int incy); */ /* not implemented ROCBLAS_EXPORT rocblas_status rocblas_chemv(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const rocblas_float_complex *alpha, const rocblas_float_complex *A, rocblas_int lda, const rocblas_float_complex *x, rocblas_int incx, const rocblas_float_complex *beta, rocblas_float_complex *y, rocblas_int incy); ROCBLAS_EXPORT rocblas_status rocblas_zhemv(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const rocblas_double_complex *alpha, const rocblas_double_complex *A, rocblas_int lda, const rocblas_double_complex *x, rocblas_int incx, const rocblas_double_complex *beta, rocblas_double_complex *y, rocblas_int incy); */ /*! \brief BLAS Level 2 API \details xGER performs the matrix-vector operations A := A + alpha*x*y**T where alpha is a scalar, x and y are vectors, and A is an m by n matrix. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] m [rocblas_int] the number of rows of the matrix A. @param[in] n [rocblas_int] the number of columns of the matrix A. @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[in] y device pointer storing vector y. @param[in] incy [rocblas_int] specifies the increment for the elements of y. @param[inout] A device pointer storing matrix A. @param[in] lda [rocblas_int] specifies the leading dimension of A. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sger(rocblas_handle handle, rocblas_int m, rocblas_int n, const float* alpha, const float* x, rocblas_int incx, const float* y, rocblas_int incy, float* A, rocblas_int lda); ROCBLAS_EXPORT rocblas_status rocblas_dger(rocblas_handle handle, rocblas_int m, rocblas_int n, const double* alpha, const double* x, rocblas_int incx, const double* y, rocblas_int incy, double* A, rocblas_int lda); /*! \brief BLAS Level 2 API \details xGER_BATCHED performs a batch of the matrix-vector operations A_i := A_i + alpha*x_i*y_i**T 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, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] m [rocblas_int] the number of rows of each matrix A_i. @param[in] n [rocblas_int] the number of columns of eaceh matrix A_i. @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each vector x_i. @param[in] y device array of device pointers storing each vector y_i. @param[in] incy [rocblas_int] specifies the increment for the elements of each vector y_i. @param[inout] A device array of device pointers storing each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sger_batched(rocblas_handle handle, rocblas_int m, rocblas_int n, const float* alpha, const float* const x[], rocblas_int incx, const float* const y[], rocblas_int incy, float* const A[], rocblas_int lda, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dger_batched(rocblas_handle handle, rocblas_int m, rocblas_int n, const double* alpha, const double* const x[], rocblas_int incx, const double* const y[], rocblas_int incy, double* const A[], rocblas_int lda, rocblas_int batch_count); /*! \brief BLAS Level 2 API \details xGER_STRIDED_BATCHED performs the matrix-vector operations A_i := A_i + alpha*x_i*y_i**T 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, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] m [rocblas_int] the number of rows of each matrix A_i. @param[in] n [rocblas_int] the number of columns of each matrix A_i. @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] x device pointer to the first vector (x_1) in the batch. @param[in] incx [rocblas_int] specifies the increments for the elements of each vector x_i. @param[in] stridex [rocblas_stride] 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 >= m * incx. @param[inout] y device pointer to the first vector (y_0) in the batch. @param[in] incy [rocblas_int] specifies the increment for the elements of each vector y_i. @param[in] stridey [rocblas_stride] 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. @param[inout] A device pointer to the first matrix (A_1) in the batch. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] strideA [rocblas_stride] stride from the start of one matrix (A_i) and the next one (A_i+1) @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sger_strided_batched(rocblas_handle handle, rocblas_int m, rocblas_int n, const float* alpha, const float* x, rocblas_int incx, rocblas_stride stridex, const float* y, rocblas_int incy, rocblas_stride stridey, float* A, rocblas_int lda, rocblas_stride strideA, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dger_strided_batched(rocblas_handle handle, rocblas_int m, rocblas_int n, const double* alpha, const double* x, rocblas_int incx, rocblas_stride stridex, const double* y, rocblas_int incy, rocblas_stride stridey, double* A, rocblas_int lda, rocblas_stride strideA, rocblas_int batch_count); /* not implemented ROCBLAS_EXPORT rocblas_status rocblas_cger(rocblas_handle handle, rocblas_int m, rocblas_int n, const rocblas_float_complex *alpha, const rocblas_float_complex *x, rocblas_int incx, const rocblas_float_complex *y, rocblas_int incy, rocblas_float_complex *A, rocblas_int lda); ROCBLAS_EXPORT rocblas_status rocblas_zger(rocblas_handle handle, rocblas_int m, rocblas_int n, const rocblas_double_complex *alpha, const rocblas_double_complex *x, rocblas_int incx, const rocblas_double_complex *y, rocblas_int incy, rocblas_double_complex *A, rocblas_int lda); */ /*! \brief BLAS Level 2 API \details xSYR performs the matrix-vector operations A := A + alpha*x*x**T where alpha is a scalar, x is a vector, and A is an n by n symmetric matrix. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] specifies whether the upper 'rocblas_fill_upper' or lower 'rocblas_fill_lower' if rocblas_fill_upper, the lower part of A is not referenced if rocblas_fill_lower, the upper part of A is not referenced @param[in] n [rocblas_int] the number of rows and columns of matrix A. @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] x device pointer storing vector x. @param[in] incx [rocblas_int] specifies the increment for the elements of x. @param[inout] A device pointer storing matrix A. @param[in] lda [rocblas_int] specifies the leading dimension of A. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_ssyr(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const float* alpha, const float* x, rocblas_int incx, float* A, rocblas_int lda); ROCBLAS_EXPORT rocblas_status rocblas_dsyr(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const double* alpha, const double* x, rocblas_int incx, double* A, rocblas_int lda); /* not implemented ROCBLAS_EXPORT rocblas_status rocblas_csyr(rocblas_handle handle, rocblas_int n, const rocblas_float_complex *alpha, const rocblas_float_complex *x, rocblas_int incx, rocblas_float_complex *A, rocblas_int lda); ROCBLAS_EXPORT rocblas_status rocblas_zsyr(rocblas_handle handle, rocblas_int n, const rocblas_double_complex *alpha, const rocblas_double_complex *x, rocblas_int incx, rocblas_double_complex *A, rocblas_int lda); */ /*! \brief BLAS Level 2 API \details xSYR_batched performs a batch of matrix-vector operations A[i] := A[i] + alpha*x[i]*x[i]**T where alpha is a scalar, x is an array of vectors, and A is an array of n by n symmetric matrices, for i = 1 , … , batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] specifies whether the upper 'rocblas_fill_upper' or lower 'rocblas_fill_lower' if rocblas_fill_upper, the lower part of A is not referenced if rocblas_fill_lower, the upper part of A is not referenced @param[in] n [rocblas_int] the number of rows and columns of matrix A. @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] x device array of device pointers storing each vector x_i. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. @param[inout] A device array of device pointers storing each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_ssyr_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const float* alpha, const float* const x[], rocblas_int incx, float* const A[], rocblas_int lda, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dsyr_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const double* alpha, const double* const x[], rocblas_int incx, double* const A[], rocblas_int lda, rocblas_int batch_count); /*! \brief BLAS Level 2 API \details xSYR_strided_batched performs the matrix-vector operations A[i] := A[i] + alpha*x[i]*x[i]**T where alpha is a scalar, vectors, and A is an array of n by n symmetric matrices, for i = 1 , … , batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] specifies whether the upper 'rocblas_fill_upper' or lower 'rocblas_fill_lower' if rocblas_fill_upper, the lower part of A is not referenced if rocblas_fill_lower, the upper part of A is not referenced @param[in] n [rocblas_int] the number of rows and columns of each matrix A. @param[in] alpha device pointer or host pointer to scalar alpha. @param[in] x device pointer to the first vector x_1. @param[in] incx [rocblas_int] specifies the increment for the elements of each x_i. @param[in] stridex [rocblas_stride] specifies the pointer increment between vectors (x_i) and (x_i+1). @param[inout] A device pointer to the first matrix A_1. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] strideA [rocblas_stride] stride from the start of one matrix (A_i) and the next one (A_i+1) @param[in] batch_count [rocblas_int] number of instances in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_ssyr_strided_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const float* alpha, const float* x, rocblas_int incx, rocblas_stride stridex, float* A, rocblas_int lda, rocblas_stride strideA, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dsyr_strided_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_int n, const double* alpha, const double* x, rocblas_int incx, rocblas_stride stridex, double* A, rocblas_int lda, rocblas_stride strideA, rocblas_int batch_count); /* * =========================================================================== * level 3 BLAS * =========================================================================== */ /*! \brief BLAS Level 3 API \details trmm performs one of the matrix-matrix operations B := alpha*op( A )*B, or B := alpha*B*op( A ) where alpha is a scalar, B is an m by n matrix, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A^T or op( A ) = A^H. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] side [rocblas_side] rocblas_side_left: C := alpha*op( A )*B. rocblas_side_right: C := alpha*B*op( A ). @param[in] uplo [rocblas_fill] rocblas_fill_upper: A is an upper triangular matrix. rocblas_fill_lower: A is a lower triangular matrix. @param[in] transA [rocblas_operation] transB: op(A) = A. rocblas_operation_transpose: op(A) = A^T. rocblas_operation_conjugate_transpose: op(A) = A^H. @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: A is assumed to be unit triangular. rocblas_diagonal_non_unit: A is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of B. m >= 0. @param[in] n [rocblas_int] n specifies the number of columns of B. n >= 0. @param[in] alpha alpha specifies the scalar alpha. When alpha is zero then A is not referenced and B need not be set before entry. @param[in] A pointer storing matrix A on the GPU. of dimension ( lda, k ), where k is m when rocblas_side_left and is n when rocblas_side_right only the upper/lower triangular part is accessed. @param[in] lda [rocblas_int] lda specifies the first dimension of A. if side = rocblas_side_left, lda >= max( 1, m ), if side = rocblas_side_right, lda >= max( 1, n ). @param[in] B pointer storing matrix B on the GPU. @param[in] ldb [rocblas_int] ldb specifies the first dimension of B. ldb >= max( 1, m ). ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strmm(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const float* alpha, const float* A, rocblas_int lda, float* B, rocblas_int ldb); ROCBLAS_EXPORT rocblas_status rocblas_dtrmm(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const double* alpha, const double* A, rocblas_int lda, double* B, rocblas_int ldb); /*! \brief BLAS Level 3 API \details trtri compute the inverse of a matrix A, namely, invA and write the result into invA; @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] specifies whether the upper 'rocblas_fill_upper' or lower 'rocblas_fill_lower' if rocblas_fill_upper, the lower part of A is not referenced if rocblas_fill_lower, the upper part of A is not referenced @param[in] diag [rocblas_diagonal] = 'rocblas_diagonal_non_unit', A is non-unit triangular; = 'rocblas_diagonal_unit', A is unit triangular; @param[in] n [rocblas_int] size of matrix A and invA @param[in] A device pointer storing matrix A. @param[in] lda [rocblas_int] specifies the leading dimension of A. @param[out] invA device pointer storing matrix invA. @param[in] ldinvA [rocblas_int] specifies the leading dimension of invA. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strtri(rocblas_handle handle, rocblas_fill uplo, rocblas_diagonal diag, rocblas_int n, const float* A, rocblas_int lda, float* invA, rocblas_int ldinvA); ROCBLAS_EXPORT rocblas_status rocblas_dtrtri(rocblas_handle handle, rocblas_fill uplo, rocblas_diagonal diag, rocblas_int n, const double* A, rocblas_int lda, double* invA, rocblas_int ldinvA); /*! \brief BLAS Level 3 API \details trtri_batched compute the inverse of A_i and write into invA_i where A_i and invA_i are the i-th matrices in the batch, for i = 1, ..., batch_count. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] specifies whether the upper 'rocblas_fill_upper' or lower 'rocblas_fill_lower' @param[in] diag [rocblas_diagonal] = 'rocblas_diagonal_non_unit', A is non-unit triangular; = 'rocblas_diagonal_unit', A is unit triangular; @param[in] n [rocblas_int] @param[in] A device array of device pointers storing each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[out] invA device array of device pointers storing the inverse of each matrix A_i. Partial inplace operation is supported, see below. If UPLO = 'U', the leading N-by-N upper triangular part of the invA will store the inverse of the upper triangular matrix, and the strictly lower triangular part of invA is cleared. If UPLO = 'L', the leading N-by-N lower triangular part of the invA will store the inverse of the lower triangular matrix, and the strictly upper triangular part of invA is cleared. @param[in] ldinvA [rocblas_int] specifies the leading dimension of each invA_i. @param[in] batch_count [rocblas_int] numbers of matrices in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strtri_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_diagonal diag, rocblas_int n, const float* const A[], rocblas_int lda, float* invA[], rocblas_int ldinvA, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dtrtri_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_diagonal diag, rocblas_int n, const double* const A[], rocblas_int lda, double* invA[], rocblas_int ldinvA, rocblas_int batch_count); /*! \brief BLAS Level 3 API \details trtri_strided_batched compute the inverse of A_i and write into invA_i where A_i and invA_i are the i-th matrices in the batch, for i = 1, ..., batch_count @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] uplo [rocblas_fill] specifies whether the upper 'rocblas_fill_upper' or lower 'rocblas_fill_lower' @param[in] diag [rocblas_diagonal] = 'rocblas_diagonal_non_unit', A is non-unit triangular; = 'rocblas_diagonal_unit', A is unit triangular; @param[in] n [rocblas_int] @param[in] A device pointer pointing to address of first matrix A_1. @param[in] lda [rocblas_int] specifies the leading dimension of each A. @param[in] stride_a [rocblas_stride] "batch stride a": stride from the start of one A_i matrix to the next A_(i + 1). @param[out] invA device pointer storing the inverses of each matrix A_i. Partial inplace operation is supported, see below. If UPLO = 'U', the leading N-by-N upper triangular part of the invA will store the inverse of the upper triangular matrix, and the strictly lower triangular part of invA is cleared. If UPLO = 'L', the leading N-by-N lower triangular part of the invA will store the inverse of the lower triangular matrix, and the strictly upper triangular part of invA is cleared. @param[in] ldinvA [rocblas_int] specifies the leading dimension of each invA_i. @param[in] stride_invA [rocblas_stride] "batch stride invA": stride from the start of one invA_i matrix to the next invA_(i + 1). @param[in] batch_count [rocblas_int] numbers of matrices in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strtri_strided_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_diagonal diag, rocblas_int n, const float* A, rocblas_int lda, rocblas_stride stride_a, float* invA, rocblas_int ldinvA, rocblas_stride stride_invA, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dtrtri_strided_batched(rocblas_handle handle, rocblas_fill uplo, rocblas_diagonal diag, rocblas_int n, const double* A, rocblas_int lda, rocblas_stride stride_a, double* invA, rocblas_int ldinvA, rocblas_stride stride_invA, rocblas_int batch_count); /*! \brief BLAS Level 3 API \details trsm solves op(A)*X = alpha*B or X*op(A) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is triangular matrix and op(A) is one of op( A ) = A or op( A ) = A^T or op( A ) = A^H. The matrix X is overwritten on B. Note about memory allocation: When trsm is launched with a k evenly divisible by the internal block size of 128, and is no larger than 10 of these blocks, the API takes advantage of utilizing pre-allocated memory found in the handle to increase overall performance. This memory can be managed by using the environment variable WORKBUF_TRSM_B_CHNK. When this variable is not set the device memory used for temporary storage will default to 1 MB and may result in chunking, which in turn may reduce performance. Under these circumstances it is recommended that WORKBUF_TRSM_B_CHNK be set to the desired chunk of right hand sides to be used at a time. (where k is m when rocblas_side_left and is n when rocblas_side_right) @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] side [rocblas_side] rocblas_side_left: op(A)*X = alpha*B. rocblas_side_right: X*op(A) = alpha*B. @param[in] uplo [rocblas_fill] rocblas_fill_upper: A is an upper triangular matrix. rocblas_fill_lower: A is a lower triangular matrix. @param[in] transA [rocblas_operation] transB: op(A) = A. rocblas_operation_transpose: op(A) = A^T. rocblas_operation_conjugate_transpose: op(A) = A^H. @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: A is assumed to be unit triangular. rocblas_diagonal_non_unit: A is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of B. m >= 0. @param[in] n [rocblas_int] n specifies the number of columns of B. n >= 0. @param[in] alpha device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced and B need not be set before entry. @param[in] A device pointer storing matrix A. of dimension ( lda, k ), where k is m when rocblas_side_left and is n when rocblas_side_right only the upper/lower triangular part is accessed. @param[in] lda [rocblas_int] lda specifies the first dimension of A. if side = rocblas_side_left, lda >= max( 1, m ), if side = rocblas_side_right, lda >= max( 1, n ). @param[in,out] B device pointer storing matrix B. @param[in] ldb [rocblas_int] ldb specifies the first dimension of B. ldb >= max( 1, m ). ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strsm(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const float* alpha, const float* A, rocblas_int lda, float* B, rocblas_int ldb); ROCBLAS_EXPORT rocblas_status rocblas_dtrsm(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const double* alpha, const double* A, rocblas_int lda, double* B, rocblas_int ldb); /*! \brief BLAS Level 3 API \details trsm_batched performs the following batched operation: op(A_i)*X_i = alpha*B_i or X_i*op(A_i) = alpha*B_i, for i = 1, ..., batch_count. where alpha is a scalar, X and B are batched m by n matrices, A is triangular batched matrix and op(A) is one of op( A ) = A or op( A ) = A^T or op( A ) = A^H. Each matrix X_i is overwritten on B_i for i = 1, ..., batch_count. Note about memory allocation: When trsm is launched with a k evenly divisible by the internal block size of 128, and is no larger than 10 of these blocks, the API takes advantage of utilizing pre-allocated memory found in the handle to increase overall performance. This memory can be managed by using the environment variable WORKBUF_TRSM_B_CHNK. When this variable is not set the device memory used for temporary storage will default to 1 MB and may result in chunking, which in turn may reduce performance. Under these circumstances it is recommended that WORKBUF_TRSM_B_CHNK be set to the desired chunk of right hand sides to be used at a time. (where k is m when rocblas_side_left and is n when rocblas_side_right) @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] side [rocblas_side] rocblas_side_left: op(A)*X = alpha*B. rocblas_side_right: X*op(A) = alpha*B. @param[in] uplo [rocblas_fill] rocblas_fill_upper: each A_i is an upper triangular matrix. rocblas_fill_lower: each A_i is a lower triangular matrix. @param[in] transA [rocblas_operation] transB: op(A) = A. rocblas_operation_transpose: op(A) = A^T. rocblas_operation_conjugate_transpose: op(A) = A^H. @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: each A_i is assumed to be unit triangular. rocblas_diagonal_non_unit: each A_i is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of each B_i. m >= 0. @param[in] n [rocblas_int] n specifies the number of columns of each B_i. n >= 0. @param[in] alpha device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced and B need not be set before entry. @param[in] A device array of device pointers storing each matrix A_i on the GPU. Matricies are of dimension ( lda, k ), where k is m when rocblas_side_left and is n when rocblas_side_right only the upper/lower triangular part is accessed. @param[in] lda [rocblas_int] lda specifies the first dimension of each A_i. if side = rocblas_side_left, lda >= max( 1, m ), if side = rocblas_side_right, lda >= max( 1, n ). @param[in,out] B device array of device pointers storing each matrix B_i on the GPU. @param[in] ldb [rocblas_int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ). @param[in] batch_count [rocblas_int] number of trsm operatons in the batch. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strsm_batched(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const float* alpha, const float* const A[], rocblas_int lda, float* B[], rocblas_int ldb, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dtrsm_batched(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const double* alpha, const double* const A[], rocblas_int lda, double* B[], rocblas_int ldb, rocblas_int batch_count); /*! \brief BLAS Level 3 API \details trsm_srided_batched performs the following strided batched operation: op(A_i)*X_i = alpha*B_i or X_i*op(A_i) = alpha*B_i, for i = 1, ..., batch_count. where alpha is a scalar, X and B are strided batched m by n matrices, A is triangular strided batched matrix and op(A) is one of op( A ) = A or op( A ) = A^T or op( A ) = A^H. Each matrix X_i is overwritten on B_i for i = 1, ..., batch_count. Note about memory allocation: When trsm is launched with a k evenly divisible by the internal block size of 128, and is no larger than 10 of these blocks, the API takes advantage of utilizing pre-allocated memory found in the handle to increase overall performance. This memory can be managed by using the environment variable WORKBUF_TRSM_B_CHNK. When this variable is not set the device memory used for temporary storage will default to 1 MB and may result in chunking, which in turn may reduce performance. Under these circumstances it is recommended that WORKBUF_TRSM_B_CHNK be set to the desired chunk of right hand sides to be used at a time. (where k is m when rocblas_side_left and is n when rocblas_side_right) @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] side [rocblas_side] rocblas_side_left: op(A)*X = alpha*B. rocblas_side_right: X*op(A) = alpha*B. @param[in] uplo [rocblas_fill] rocblas_fill_upper: each A_i is an upper triangular matrix. rocblas_fill_lower: each A_i is a lower triangular matrix. @param[in] transA [rocblas_operation] transB: op(A) = A. rocblas_operation_transpose: op(A) = A^T. rocblas_operation_conjugate_transpose: op(A) = A^H. @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: each A_i is assumed to be unit triangular. rocblas_diagonal_non_unit: each A_i is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of each B_i. m >= 0. @param[in] n [rocblas_int] n specifies the number of columns of each B_i. n >= 0. @param[in] alpha device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced and B need not be set before entry. @param[in] A device pointer pointing to the first matrix A_1. of dimension ( lda, k ), where k is m when rocblas_side_left and is n when rocblas_side_right only the upper/lower triangular part is accessed. @param[in] lda [rocblas_int] lda specifies the first dimension of each A_i. if side = rocblas_side_left, lda >= max( 1, m ), if side = rocblas_side_right, lda >= max( 1, n ). @param[in] stride_a [rocblas_stride] stride from the start of one A_i matrix to the next A_(i + 1). @param[in,out] B device pointer pointing to the first matrix B_1. @param[in] ldb [rocblas_int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ). @param[in] stride_b [rocblas_stride] stride from the start of one B_i matrix to the next B_(i + 1). @param[in] batch_count [rocblas_int] number of trsm operatons in the batch. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_strsm_strided_batched(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const float* alpha, const float* A, rocblas_int lda, rocblas_stride stride_a, float* B, rocblas_int ldb, rocblas_stride stride_b, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dtrsm_strided_batched(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const double* alpha, const double* A, rocblas_int lda, rocblas_stride stride_a, double* B, rocblas_int ldb, rocblas_stride stride_b, rocblas_int batch_count); /*! \brief BLAS Level 3 API \details xGEMM performs one of the matrix-matrix operations C = alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] transA [rocblas_operation] specifies the form of op( A ) @param[in] transB [rocblas_operation] specifies the form of op( B ) @param[in] m [rocblas_int] number or rows of matrices op( A ) and C @param[in] n [rocblas_int] number of columns of matrices op( B ) and C @param[in] k [rocblas_int] number of columns of matrix op( A ) and number of rows of matrix op( B ) @param[in] alpha device pointer or host pointer specifying the scalar alpha. @param[in] A device pointer storing matrix A. @param[in] lda [rocblas_int] specifies the leading dimension of A. @param[in] B device pointer storing matrix B. @param[in] ldb [rocblas_int] specifies the leading dimension of B. @param[in] beta device pointer or host pointer specifying the scalar beta. @param[in, out] C device pointer storing matrix C on the GPU. @param[in] ldc [rocblas_int] specifies the leading dimension of C. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sgemm(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const float* alpha, const float* A, rocblas_int lda, const float* B, rocblas_int ldb, const float* beta, float* C, rocblas_int ldc); ROCBLAS_EXPORT rocblas_status rocblas_dgemm(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const double* alpha, const double* A, rocblas_int lda, const double* B, rocblas_int ldb, const double* beta, double* C, rocblas_int ldc); ROCBLAS_EXPORT rocblas_status rocblas_hgemm(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_half* alpha, const rocblas_half* A, rocblas_int lda, const rocblas_half* B, rocblas_int ldb, const rocblas_half* beta, rocblas_half* C, rocblas_int ldc); /* not implemented ROCBLAS_EXPORT rocblas_status rocblas_qgemm( rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_half_complex *alpha, const rocblas_half_complex *A, rocblas_int lda, const rocblas_half_complex *B, rocblas_int ldb, const rocblas_half_complex *beta, rocblas_half_complex *C, rocblas_int ldc); */ ROCBLAS_EXPORT rocblas_status rocblas_cgemm(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_float_complex* alpha, const rocblas_float_complex* A, rocblas_int lda, const rocblas_float_complex* B, rocblas_int ldb, const rocblas_float_complex* beta, rocblas_float_complex* C, rocblas_int ldc); ROCBLAS_EXPORT rocblas_status rocblas_zgemm(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_double_complex* alpha, const rocblas_double_complex* A, rocblas_int lda, const rocblas_double_complex* B, rocblas_int ldb, const rocblas_double_complex* beta, rocblas_double_complex* C, rocblas_int ldc); /*! \brief BLAS Level 3 API \details xGEMM_BATCHED performs one of the batched matrix-matrix operations C_i = alpha*op( A_i )*op( B_i ) + beta*C_i, for i = 1, ..., batch_count. 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 batch_count strided_batched matrix, op( B ) an k by n by batch_count strided_batched matrix and C an m by n by batch_count strided_batched matrix. @param[in] handle [rocblas_handle handle to the rocblas library context queue. @param[in] transA [rocblas_operation] specifies the form of op( A ) @param[in] transB [rocblas_operation] specifies the form of op( B ) @param[in] m [rocblas_int] matrix dimention m. @param[in] n [rocblas_int] matrix dimention n. @param[in] k [rocblas_int] matrix dimention k. @param[in] alpha device pointer or host pointer specifying the scalar alpha. @param[in] A device array of device pointers storing each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] B device array of device pointers storing each matrix B_i. @param[in] ldb [rocblas_int] specifies the leading dimension of each B_i. @param[in] beta device pointer or host pointer specifying the scalar beta. @param[in, out] C device array of device pointers storing each matrix C_i. @param[in] ldc [rocblas_int] specifies the leading dimension of each C_i. @param[in] batch_count [rocblas_int] number of gemm operations in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sgemm_batched(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const float* alpha, const float* const A[], rocblas_int lda, const float* const B[], rocblas_int ldb, const float* beta, float* const C[], rocblas_int ldc, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dgemm_batched(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const double* alpha, const double* const A[], rocblas_int lda, const double* const B[], rocblas_int ldb, const double* beta, double* const C[], rocblas_int ldc, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_hgemm_batched(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_half* alpha, const rocblas_half* const A[], rocblas_int lda, const rocblas_half* const B[], rocblas_int ldb, const rocblas_half* beta, rocblas_half* const C[], rocblas_int ldc, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_cgemm_batched(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_float_complex* alpha, const rocblas_float_complex* const A[], rocblas_int lda, const rocblas_float_complex* const B[], rocblas_int ldb, const rocblas_float_complex* beta, rocblas_float_complex* const C[], rocblas_int ldc, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zgemm_batched(rocblas_handle handle, rocblas_operation transa, rocblas_operation transb, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_double_complex* alpha, const rocblas_double_complex* const A[], rocblas_int lda, const rocblas_double_complex* const B[], rocblas_int ldb, const rocblas_double_complex* beta, rocblas_double_complex* const C[], rocblas_int ldc, rocblas_int batch_count); /*************************************************************************** * batched * stride_a - "batch stride a": stride from the start of one "A" matrix to the next * stride_b * stride_c * batch_count - numbers of gemm's in the batch **************************************************************************/ /*! \brief BLAS Level 3 API \details xGEMM_STRIDED_BATCHED performs one of the strided batched matrix-matrix operations C_i = alpha*op( A_i )*op( B_i ) + beta*C_i, for i = 1, ..., batch_count. 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 batch_count strided_batched matrix, op( B ) an k by n by batch_count strided_batched matrix and C an m by n by batch_count strided_batched matrix. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] transA [rocblas_operation] specifies the form of op( A ) @param[in] transB [rocblas_operation] specifies the form of op( B ) @param[in] m [rocblas_int] matrix dimention m. @param[in] n [rocblas_int] matrix dimention n. @param[in] k [rocblas_int] matrix dimention k. @param[in] alpha device pointer or host pointer specifying the scalar alpha. @param[in] A device pointer pointing to the first matrix A_1. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] stride_a [rocblas_stride] stride from the start of one A_i matrix to the next A_(i + 1). @param[in] B device pointer pointing to the first matrix B_1. @param[in] ldb [rocblas_int] specifies the leading dimension of each B_i. @param[in] stride_b [rocblas_stride] stride from the start of one B_i matrix to the next B_(i + 1). @param[in] beta device pointer or host pointer specifying the scalar beta. @param[in, out] C device pointer pointing to the first matrix C_1. @param[in] ldc [rocblas_int] specifies the leading dimension of each C_i. @param[in] stride_c [rocblas_stride] stride from the start of one C_i matrix to the next C_(i + 1). @param[in] batch_count [rocblas_int] number of gemm operatons in the batch ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sgemm_strided_batched(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const float* alpha, const float* A, rocblas_int lda, rocblas_stride stride_a, const float* B, rocblas_int ldb, rocblas_stride stride_b, const float* beta, float* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dgemm_strided_batched(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const double* alpha, const double* A, rocblas_int lda, rocblas_stride stride_a, const double* B, rocblas_int ldb, rocblas_stride stride_b, const double* beta, double* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_hgemm_strided_batched(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_half* alpha, const rocblas_half* A, rocblas_int lda, rocblas_stride stride_a, const rocblas_half* B, rocblas_int ldb, rocblas_stride stride_b, const rocblas_half* beta, rocblas_half* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_hgemm_kernel_name(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_half* alpha, const rocblas_half* A, rocblas_int lda, rocblas_stride stride_a, const rocblas_half* B, rocblas_int ldb, rocblas_stride stride_b, const rocblas_half* beta, rocblas_half* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_sgemm_kernel_name(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const float* alpha, const float* A, rocblas_int lda, rocblas_stride stride_a, const float* B, rocblas_int ldb, rocblas_stride stride_b, const float* beta, float* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_dgemm_kernel_name(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const double* alpha, const double* A, rocblas_int lda, rocblas_stride stride_a, const double* B, rocblas_int ldb, rocblas_stride stride_b, const double* beta, double* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); /* not implemented ROCBLAS_EXPORT rocblas_status rocblas_qgemm_strided_batched( rocblas_handle handle, rocblas_operation transa, rocblas_operation transb, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_half_complex *alpha, const rocblas_half_complex *A, rocblas_int lda, rocblas_stride stride_a, const rocblas_half_complex *B, rocblas_int ldb, rocblas_stride stride_b, const rocblas_half_complex *beta, rocblas_half_complex *C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count ); */ ROCBLAS_EXPORT rocblas_status rocblas_cgemm_strided_batched(rocblas_handle handle, rocblas_operation transa, rocblas_operation transb, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_float_complex* alpha, const rocblas_float_complex* A, rocblas_int lda, rocblas_stride stride_a, const rocblas_float_complex* B, rocblas_int ldb, rocblas_stride stride_b, const rocblas_float_complex* beta, rocblas_float_complex* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); ROCBLAS_EXPORT rocblas_status rocblas_zgemm_strided_batched(rocblas_handle handle, rocblas_operation transa, rocblas_operation transb, rocblas_int m, rocblas_int n, rocblas_int k, const rocblas_double_complex* alpha, const rocblas_double_complex* A, rocblas_int lda, rocblas_stride stride_a, const rocblas_double_complex* B, rocblas_int ldb, rocblas_stride stride_b, const rocblas_double_complex* beta, rocblas_double_complex* C, rocblas_int ldc, rocblas_stride stride_c, rocblas_int batch_count); /*! \brief BLAS Level 3 API \details xGEAM performs one of the matrix-matrix operations C = alpha*op( A ) + beta*op( B ), where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by n matrix, op( B ) an m by n matrix, and C an m by n matrix. @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] transA [rocblas_operation] specifies the form of op( A ) @param[in] transB [rocblas_operation] specifies the form of op( B ) @param[in] m [rocblas_int] matrix dimension m. @param[in] n [rocblas_int] matrix dimension n. @param[in] alpha device pointer or host pointer specifying the scalar alpha. @param[in] A device pointer storing matrix A. @param[in] lda [rocblas_int] specifies the leading dimension of A. @param[in] beta device pointer or host pointer specifying the scalar beta. @param[in] B device pointer storing matrix B. @param[in] ldb [rocblas_int] specifies the leading dimension of B. @param[in, out] C device pointer storing matrix C. @param[in] ldc [rocblas_int] specifies the leading dimension of C. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_sgeam(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, const float* alpha, const float* A, rocblas_int lda, const float* beta, const float* B, rocblas_int ldb, float* C, rocblas_int ldc); ROCBLAS_EXPORT rocblas_status rocblas_dgeam(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, const double* alpha, const double* A, rocblas_int lda, const double* beta, const double* B, rocblas_int ldb, double* C, rocblas_int ldc); /* * =========================================================================== * BLAS extensions * =========================================================================== */ /*! \brief BLAS EX API \details GEMM_EX performs one of the matrix-matrix operations D = alpha*op( A )*op( B ) + beta*C, where op( X ) is one of op( X ) = X or op( X ) = X**T or op( X ) = X**H, alpha and beta are scalars, and A, B, C, and D are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C and D are m by n matrices. Supported types are as follows: - rocblas_datatype_f64_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f32_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f16_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f16_r = a_type = b_type = c_type = d_type; rocblas_datatype_f32_r = compute_type - rocblas_datatype_bf16_r = a_type = b_type = c_type = d_type; rocblas_datatype_f32_r = compute_type - rocblas_datatype_i8_r = a_type = b_type; rocblas_datatype_i32_r = c_type = d_type = compute_type - rocblas_datatype_f32_c = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f64_c = a_type = b_type = c_type = d_type = compute_type Below are restrictions for rocblas_datatype_i8_r = a_type = b_type; rocblas_datatype_i32_r = c_type = d_type = compute_type: - k must be a multiple of 4 - lda must be a multiple of 4 if transA == rocblas_operation_transpose - ldb must be a multiple of 4 if transB == rocblas_operation_none - for transA == rocblas_operation_transpose or transB == rocblas_operation_none the matrices A and B must have each 4 consecutive values in the k dimension packed. This packing can be achieved with the following pseudo-code. The code assumes the original matrices are in A and B, and the packed matrices are A_packed and B_packed. The size of the A_packed matrix is the same as the size of the A matrix, and the size of the B_packed matrix is the same as the size of the B matrix. @code if(transA == rocblas_operation_none) { int nb = 4; for(int i_m = 0; i_m < m; i_m++) { for(int i_k = 0; i_k < k; i_k++) { A_packed[i_k % nb + (i_m + (i_k / nb) * lda) * nb] = A[i_m + i_k * lda]; } } } else { A_packed = A; } if(transB == rocblas_operation_transpose) { int nb = 4; for(int i_n = 0; i_n < m; i_n++) { for(int i_k = 0; i_k < k; i_k++) { B_packed[i_k % nb + (i_n + (i_k / nb) * lda) * nb] = B[i_n + i_k * lda]; } } } else { B_packed = B; } @endcode @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] transA [rocblas_operation] specifies the form of op( A ). @param[in] transB [rocblas_operation] specifies the form of op( B ). @param[in] m [rocblas_int] matrix dimension m. @param[in] n [rocblas_int] matrix dimension n. @param[in] k [rocblas_int] matrix dimension k. @param[in] alpha [const void *] device pointer or host pointer specifying the scalar alpha. Same datatype as compute_type. @param[in] a [void *] device pointer storing matrix A. @param[in] a_type [rocblas_datatype] specifies the datatype of matrix A. @param[in] lda [rocblas_int] specifies the leading dimension of A. @param[in] b [void *] device pointer storing matrix B. @param[in] b_type [rocblas_datatype] specifies the datatype of matrix B. @param[in] ldb [rocblas_int] specifies the leading dimension of B. @param[in] beta [const void *] device pointer or host pointer specifying the scalar beta. Same datatype as compute_type. @param[in] c [void *] device pointer storing matrix C. @param[in] c_type [rocblas_datatype] specifies the datatype of matrix C. @param[in] ldc [rocblas_int] specifies the leading dimension of C. @param[out] d [void *] device pointer storing matrix D. @param[in] d_type [rocblas_datatype] specifies the datatype of matrix D. @param[in] ldd [rocblas_int] specifies the leading dimension of D. @param[in] compute_type [rocblas_datatype] specifies the datatype of computation. @param[in] algo [rocblas_gemm_algo] enumerant specifying the algorithm type. @param[in] solution_index [int32_t] reserved for future use. @param[in] flags [uint32_t] reserved for future use. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_gemm_ex(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const void* alpha, const void* a, rocblas_datatype a_type, rocblas_int lda, const void* b, rocblas_datatype b_type, rocblas_int ldb, const void* beta, const void* c, rocblas_datatype c_type, rocblas_int ldc, void* d, rocblas_datatype d_type, rocblas_int ldd, rocblas_datatype compute_type, rocblas_gemm_algo algo, int32_t solution_index, uint32_t flags); /* For backward compatiblity, unused workspace_size and workspace arguments are ignored */ // clang-format off #define rocblas_gemm_ex(handle, \ transA, \ transB, \ m, \ n, \ k, \ alpha, \ a, \ a_type, \ lda, \ b, \ b_type, \ ldb, \ beta, \ c, \ c_type, \ ldc, \ d, \ d_type, \ ldd, \ compute_type, \ algo, \ solution_index, \ flags, \ ...) \ ROCBLAS_VA_OPT_PRAGMA(GCC warning "rocblas_gemm_ex: The workspace_size and workspace arguments are obsolete, and will be ignored", __VA_ARGS__) \ rocblas_gemm_ex(handle, \ transA, \ transB, \ m, \ n, \ k, \ alpha, \ a, \ a_type, \ lda, \ b, \ b_type, \ ldb, \ beta, \ c, \ c_type, \ ldc, \ d, \ d_type, \ ldd, \ compute_type, \ algo, \ solution_index, \ flags) // clang-format on /*! \brief BLAS EX API \details GEMM_BATCHED_EX performs one of the batched matrix-matrix operations D_i = alpha*op(A_i)*op(B_i) + beta*C_i, for i = 1, ..., batch_count. 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, C, and D are batched pointers to matrices, with op( A ) an m by k by batch_count batched matrix, op( B ) a k by n by batch_count batched matrix and C and D are m by n by batch_count batched matrices. The batched matrices are an array of pointers to matrices. The number of pointers to matrices is batch_count. Supported types are as follows: - rocblas_datatype_f64_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f32_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f16_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f16_r = a_type = b_type = c_type = d_type; rocblas_datatype_f32_r = compute_type - rocblas_datatype_bf16_r = a_type = b_type = c_type = d_type; rocblas_datatype_f32_r = compute_type - rocblas_datatype_i8_r = a_type = b_type; rocblas_datatype_i32_r = c_type = d_type = compute_type - rocblas_datatype_f32_c = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f64_c = a_type = b_type = c_type = d_type = compute_type Below are restrictions for rocblas_datatype_i8_r = a_type = b_type; rocblas_datatype_i32_r = c_type = d_type = compute_type: - k must be a multiple of 4 - lda must be a multiple of 4 if transA == rocblas_operation_transpose - ldb must be a multiple of 4 if transB == rocblas_operation_none - for transA == rocblas_operation_transpose or transB == rocblas_operation_none the matrices A and B must have each 4 consecutive values in the k dimension packed. This packing can be achieved with the following pseudo-code. The code assumes the original matrices are in A and B, and the packed matrices are A_packed and B_packed. The size of the A_packed matrix is the same as the size of the A matrix, and the size of the B_packed matrix is the same as the size of the B matrix. @code if(transA == rocblas_operation_none) { int nb = 4; for(int i_m = 0; i_m < m; i_m++) { for(int i_k = 0; i_k < k; i_k++) { A_packed[i_k % nb + (i_m + (i_k / nb) * lda) * nb] = A[i_m + i_k * lda]; } } } else { A_packed = A; } if(transB == rocblas_operation_transpose) { int nb = 4; for(int i_n = 0; i_n < m; i_n++) { for(int i_k = 0; i_k < k; i_k++) { B_packed[i_k % nb + (i_n + (i_k / nb) * lda) * nb] = B[i_n + i_k * lda]; } } } else { B_packed = B; } @endcode @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] transA [rocblas_operation] specifies the form of op( A ). @param[in] transB [rocblas_operation] specifies the form of op( B ). @param[in] m [rocblas_int] matrix dimension m. @param[in] n [rocblas_int] matrix dimension n. @param[in] k [rocblas_int] matrix dimension k. @param[in] alpha [const void *] device pointer or host pointer specifying the scalar alpha. Same datatype as compute_type. @param[in] a [void *] device pointer storing array of pointers to each matrix A_i. @param[in] a_type [rocblas_datatype] specifies the datatype of each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] b [void *] device pointer storing array of pointers to each matrix B_i. @param[in] b_type [rocblas_datatype] specifies the datatype of each matrix B_i. @param[in] ldb [rocblas_int] specifies the leading dimension of each B_i. @param[in] beta [const void *] device pointer or host pointer specifying the scalar beta. Same datatype as compute_type. @param[in] c [void *] device array of device pointers to each matrix C_i. @param[in] c_type [rocblas_datatype] specifies the datatype of each matrix C_i. @param[in] ldc [rocblas_int] specifies the leading dimension of each C_i. @param[out] d [void *] device array of device pointers to each matrix D_i. @param[in] d_type [rocblas_datatype] specifies the datatype of each matrix D_i. @param[in] ldd [rocblas_int] specifies the leading dimension of each D_i. @param[in] batch_count [rocblas_int] number of gemm operations in the batch. @param[in] compute_type [rocblas_datatype] specifies the datatype of computation. @param[in] algo [rocblas_gemm_algo] enumerant specifying the algorithm type. @param[in] solution_index [int32_t] reserved for future use. @param[in] flags [uint32_t] reserved for future use. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_gemm_batched_ex(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const void* alpha, const void* a, rocblas_datatype a_type, rocblas_int lda, const void* b, rocblas_datatype b_type, rocblas_int ldb, const void* beta, const void* c, rocblas_datatype c_type, rocblas_int ldc, void* d, rocblas_datatype d_type, rocblas_int ldd, rocblas_int batch_count, rocblas_datatype compute_type, rocblas_gemm_algo algo, int32_t solution_index, uint32_t flags); /*! \brief BLAS EX API \details GEMM_STRIDED_BATCHED_EX performs one of the strided_batched matrix-matrix operations D_i = alpha*op(A_i)*op(B_i) + beta*C_i, for i = 1, ..., batch_count 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, C, and D are strided_batched matrices, with op( A ) an m by k by batch_count strided_batched matrix, op( B ) a k by n by batch_count strided_batched matrix and C and D are m by n by batch_count strided_batched matrices. The strided_batched matrices are multiple matrices separated by a constant stride. The number of matrices is batch_count. Supported types are as follows: - rocblas_datatype_f64_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f32_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f16_r = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f16_r = a_type = b_type = c_type = d_type; rocblas_datatype_f32_r = compute_type - rocblas_datatype_bf16_r = a_type = b_type = c_type = d_type; rocblas_datatype_f32_r = compute_type - rocblas_datatype_i8_r = a_type = b_type; rocblas_datatype_i32_r = c_type = d_type = compute_type - rocblas_datatype_f32_c = a_type = b_type = c_type = d_type = compute_type - rocblas_datatype_f64_c = a_type = b_type = c_type = d_type = compute_type Below are restrictions for rocblas_datatype_i8_r = a_type = b_type; rocblas_datatype_i32_r = c_type = d_type = compute_type: - k must be a multiple of 4 - lda must be a multiple of 4 if transA == rocblas_operation_transpose - ldb must be a multiple of 4 if transB == rocblas_operation_none - for transA == rocblas_operation_transpose or transB == rocblas_operation_none the matrices A and B must have each 4 consecutive values in the k dimension packed. This packing can be achieved with the following pseudo-code. The code assumes the original matrices are in A and B, and the packed matrices are A_packed and B_packed. The size of the A_packed matrix is the same as the size of the A matrix, and the size of the B_packed matrix is the same as the size of the B matrix. @code if(transA == rocblas_operation_none) { int nb = 4; for(int i_m = 0; i_m < m; i_m++) { for(int i_k = 0; i_k < k; i_k++) { A_packed[i_k % nb + (i_m + (i_k / nb) * lda) * nb] = A[i_m + i_k * lda]; } } } else { A_packed = A; } if(transB == rocblas_operation_transpose) { int nb = 4; for(int i_n = 0; i_n < m; i_n++) { for(int i_k = 0; i_k < k; i_k++) { B_packed[i_k % nb + (i_n + (i_k / nb) * lda) * nb] = B[i_n + i_k * lda]; } } } else { B_packed = B; } @endcode @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] transA [rocblas_operation] specifies the form of op( A ). @param[in] transB [rocblas_operation] specifies the form of op( B ). @param[in] m [rocblas_int] matrix dimension m. @param[in] n [rocblas_int] matrix dimension n. @param[in] k [rocblas_int] matrix dimension k. @param[in] alpha [const void *] device pointer or host pointer specifying the scalar alpha. Same datatype as compute_type. @param[in] a [void *] device pointer pointing to first matrix A_1. @param[in] a_type [rocblas_datatype] specifies the datatype of each matrix A_i. @param[in] lda [rocblas_int] specifies the leading dimension of each A_i. @param[in] stride_a [rocblas_stride] specifies stride from start of one A_i matrix to the next A_(i + 1). @param[in] b [void *] device pointer pointing to first matrix B_1. @param[in] b_type [rocblas_datatype] specifies the datatype of each matrix B_i. @param[in] ldb [rocblas_int] specifies the leading dimension of each B_i. @param[in] stride_b [rocblas_stride] specifies stride from start of one B_i matrix to the next B_(i + 1). @param[in] beta [const void *] device pointer or host pointer specifying the scalar beta. Same datatype as compute_type. @param[in] c [void *] device pointer pointing to first matrix C_1. @param[in] c_type [rocblas_datatype] specifies the datatype of each matrix C_i. @param[in] ldc [rocblas_int] specifies the leading dimension of each C_i. @param[in] stride_c [rocblas_stride] specifies stride from start of one C_i matrix to the next C_(i + 1). @param[out] d [void *] device pointer storing each matrix D_i. @param[in] d_type [rocblas_datatype] specifies the datatype of each matrix D_i. @param[in] ldd [rocblas_int] specifies the leading dimension of each D_i. @param[in] stride_d [rocblas_stride] specifies stride from start of one D_i matrix to the next D_(i + 1). @param[in] batch_count [rocblas_int] number of gemm operations in the batch. @param[in] compute_type [rocblas_datatype] specifies the datatype of computation. @param[in] algo [rocblas_gemm_algo] enumerant specifying the algorithm type. @param[in] solution_index [int32_t] reserved for future use. @param[in] flags [uint32_t] reserved for future use. ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_gemm_strided_batched_ex(rocblas_handle handle, rocblas_operation transA, rocblas_operation transB, rocblas_int m, rocblas_int n, rocblas_int k, const void* alpha, const void* a, rocblas_datatype a_type, rocblas_int lda, rocblas_stride stride_a, const void* b, rocblas_datatype b_type, rocblas_int ldb, rocblas_stride stride_b, const void* beta, const void* c, rocblas_datatype c_type, rocblas_int ldc, rocblas_stride stride_c, void* d, rocblas_datatype d_type, rocblas_int ldd, rocblas_stride stride_d, rocblas_int batch_count, rocblas_datatype compute_type, rocblas_gemm_algo algo, int32_t solution_index, uint32_t flags); /* For backward compatiblity, unused workspace_size and workspace arguments are ignored */ // clang-format off #define rocblas_gemm_strided_batched_ex(handle, \ transA, \ transB, \ m, \ n, \ k, \ alpha, \ a, \ a_type, \ lda, \ stride_a, \ b, \ b_type, \ ldb, \ stride_b, \ beta, \ c, \ c_type, \ ldc, \ stride_c, \ d, \ d_type, \ ldd, \ stride_d, \ batch_count, \ compute_type, \ algo, \ solution_index, \ flags, \ ...) \ ROCBLAS_VA_OPT_PRAGMA(GCC warning "rocblas_gemm_strided_batched_ex: The workspace_size and workspace arguments are obsolete, and will be ignored", __VA_ARGS__) \ rocblas_gemm_strided_batched_ex(handle, \ transA, \ transB, \ m, \ n, \ k, \ alpha, \ a, \ a_type, \ lda, \ stride_a, \ b, \ b_type, \ ldb, \ stride_b, \ beta, \ c, \ c_type, \ ldc, \ stride_c, \ d, \ d_type, \ ldd, \ stride_d, \ batch_count, \ compute_type, \ algo, \ solution_index, \ flags) // clang-format on /*! BLAS EX API \details TRSM_EX solves op(A)*X = alpha*B or X*op(A) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is triangular matrix and op(A) is one of op( A ) = A or op( A ) = A^T or op( A ) = A^H. The matrix X is overwritten on B. TRSM_EX gives the user the ability to reuse the invA matrix between runs. If invA == NULL, rocblas_trsm_ex will automatically calculate invA on every run. Setting up invA: The accepted invA matrix consists of the packed 128x128 inverses of the diagonal blocks of matrix A, followed by any smaller diagonal block that remains. To set up invA it is recommended that rocblas_trtri_batched be used with matrix A as the input. Device memory of size 128 x k should be allocated for invA ahead of time, where k is m when rocblas_side_left and is n when rocblas_side_right. The actual number of elements in invA should be passed as invA_size. To begin, rocblas_trtri_batched must be called on the full 128x128 sized diagonal blocks of matrix A. Below are the restricted parameters: - n = 128 - ldinvA = 128 - stride_invA = 128x128 - batch_count = k / 128, Then any remaining block may be added: - n = k % 128 - invA = invA + stride_invA * previous_batch_count - ldinvA = 128 - batch_count = 1 @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] side [rocblas_side] rocblas_side_left: op(A)*X = alpha*B. rocblas_side_right: X*op(A) = alpha*B. @param[in] uplo [rocblas_fill] rocblas_fill_upper: A is an upper triangular matrix. rocblas_fill_lower: A is a lower triangular matrix. @param[in] transA [rocblas_operation] transB: op(A) = A. rocblas_operation_transpose: op(A) = A^T. rocblas_operation_conjugate_transpose: op(A) = A^H. @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: A is assumed to be unit triangular. rocblas_diagonal_non_unit: A is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of B. m >= 0. @param[in] n [rocblas_int] n specifies the number of columns of B. n >= 0. @param[in] alpha [void *] device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced, and B need not be set before entry. @param[in] A [void *] device pointer storing matrix A. of dimension ( lda, k ), where k is m when rocblas_side_left and is n when rocblas_side_right only the upper/lower triangular part is accessed. @param[in] lda [rocblas_int] lda specifies the first dimension of A. if side = rocblas_side_left, lda >= max( 1, m ), if side = rocblas_side_right, lda >= max( 1, n ). @param[in, out] B [void *] device pointer storing matrix B. B is of dimension ( ldb, n ). Before entry, the leading m by n part of the array B must contain the right-hand side matrix B, and on exit is overwritten by the solution matrix X. @param[in] ldb [rocblas_int] ldb specifies the first dimension of B. ldb >= max( 1, m ). @param[in] invA [void *] device pointer storing the inverse diagonal blocks of A. invA is of dimension ( ld_invA, k ), where k is m when rocblas_side_left and is n when rocblas_side_right. ld_invA must be equal to 128. @param[in] invA_size [rocblas_int] invA_size specifies the number of elements of device memory in invA. @param[in] compute_type [rocblas_datatype] specifies the datatype of computation ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_trsm_ex(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const void* alpha, const void* A, rocblas_int lda, void* B, rocblas_int ldb, const void* invA, rocblas_int invA_size, rocblas_datatype compute_type); /* For backward compatiblity, option, x_temp_size and x_temp_workspace arguments are ignored */ // clang-format off #define rocblas_trsm_ex(handle, \ side, \ uplo, \ transA, \ diag, \ m, \ n, \ alpha, \ A, \ lda, \ B, \ ldb, \ invA, \ invA_size, \ compute_type, \ ...) \ ROCBLAS_VA_OPT_PRAGMA(GCC warning "rocblas_trsm_ex: The option, x_temp_size and x_temp_workspace arguments are obsolete, and will be ignored", __VA_ARGS__) \ rocblas_trsm_ex(handle, \ side, \ uplo, \ transA, \ diag, \ m, \ n, \ alpha, \ A, \ lda, \ B, \ ldb, \ invA, \ invA_size, \ compute_type) // clang-format on /*! BLAS EX API \details TRSM_BATCHED_EX solves op(A_i)*X_i = alpha*B_i or X_i*op(A_i) = alpha*B_i, for i = 1, ..., batch_count; and where alpha is a scalar, X and B are arrays of m by n matrices, A is an array of triangular matrix and each op(A_i) is one of op( A_i ) = A_i or op( A_i ) = A_i^T or op( A_i ) = A_i^H. Each matrix X_i is overwritten on B_i. TRSM_EX gives the user the ability to reuse the invA matrix between runs. If invA == NULL, rocblas_trsm_batched_ex will automatically calculate each invA_i on every run. Setting up invA: Each accepted invA_i matrix consists of the packed 128x128 inverses of the diagonal blocks of matrix A_i, followed by any smaller diagonal block that remains. To set up each invA_i it is recommended that rocblas_trtri_batched be used with matrix A_i as the input. invA is an array of pointers of batch_count length holding each invA_i. Device memory of size 128 x k should be allocated for each invA_i ahead of time, where k is m when rocblas_side_left and is n when rocblas_side_right. The actual number of elements in each invA_i should be passed as invA_size. To begin, rocblas_trtri_batched must be called on the full 128x128 sized diagonal blocks of each matrix A_i. Below are the restricted parameters: - n = 128 - ldinvA = 128 - stride_invA = 128x128 - batch_count = k / 128, Then any remaining block may be added: - n = k % 128 - invA = invA + stride_invA * previous_batch_count - ldinvA = 128 - batch_count = 1 @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] side [rocblas_side] rocblas_side_left: op(A)*X = alpha*B. rocblas_side_right: X*op(A) = alpha*B. @param[in] uplo [rocblas_fill] rocblas_fill_upper: each A_i is an upper triangular matrix. rocblas_fill_lower: each A_i is a lower triangular matrix. @param[in] transA [rocblas_operation] transB: op(A) = A. rocblas_operation_transpose: op(A) = A^T. rocblas_operation_conjugate_transpose: op(A) = A^H. @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: each A_i is assumed to be unit triangular. rocblas_diagonal_non_unit: each A_i is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of each B_i. m >= 0. @param[in] n [rocblas_int] n specifies the number of columns of each B_i. n >= 0. @param[in] alpha [void *] device pointer or host pointer alpha specifying the scalar alpha. When alpha is &zero then A is not referenced, and B need not be set before entry. @param[in] A [void *] device array of device pointers storing each matrix A_i. each A_i is of dimension ( lda, k ), where k is m when rocblas_side_left and is n when rocblas_side_right only the upper/lower triangular part is accessed. @param[in] lda [rocblas_int] lda specifies the first dimension of each A_i. if side = rocblas_side_left, lda >= max( 1, m ), if side = rocblas_side_right, lda >= max( 1, n ). @param[in, out] B [void *] device array of device pointers storing each matrix B_i. each B_i is of dimension ( ldb, n ). Before entry, the leading m by n part of the array B_i must contain the right-hand side matrix B_i, and on exit is overwritten by the solution matrix X_i @param[in] ldb [rocblas_int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ). @param[in] batch_count [rocblas_int] specifies how many batches. @param[in] invA [void *] device array of device pointers storing the inverse diagonal blocks of each A_i. each invA_i is of dimension ( ld_invA, k ), where k is m when rocblas_side_left and is n when rocblas_side_right. ld_invA must be equal to 128. @param[in] invA_size [rocblas_int] invA_size specifies the number of elements of device memory in each invA_i. @param[in] compute_type [rocblas_datatype] specifies the datatype of computation ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_trsm_batched_ex(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const void* alpha, const void* A, rocblas_int lda, void* B, rocblas_int ldb, rocblas_int batch_count, const void* invA, rocblas_int invA_size, rocblas_datatype compute_type); /*! BLAS EX API \details TRSM_STRIDED_BATCHED_EX solves op(A_i)*X_i = alpha*B_i or X_i*op(A_i) = alpha*B_i, for i = 1, ..., batch_count; and where alpha is a scalar, X and B are strided batched m by n matrices, A is a strided batched triangular matrix and op(A_i) is one of op( A_i ) = A_i or op( A_i ) = A_i^T or op( A_i ) = A_i^H. Each matrix X_i is overwritten on B_i. TRSM_EX gives the user the ability to reuse each invA_i matrix between runs. If invA == NULL, rocblas_trsm_batched_ex will automatically calculate each invA_i on every run. Setting up invA: Each accepted invA_i matrix consists of the packed 128x128 inverses of the diagonal blocks of matrix A_i, followed by any smaller diagonal block that remains. To set up invA_i it is recommended that rocblas_trtri_batched be used with matrix A_i as the input. invA is a contiguous piece of memory holding each invA_i. Device memory of size 128 x k should be allocated for each invA_i ahead of time, where k is m when rocblas_side_left and is n when rocblas_side_right. The actual number of elements in each invA_i should be passed as invA_size. To begin, rocblas_trtri_batched must be called on the full 128x128 sized diagonal blocks of each matrix A_i. Below are the restricted parameters: - n = 128 - ldinvA = 128 - stride_invA = 128x128 - batch_count = k / 128, Then any remaining block may be added: - n = k % 128 - invA = invA + stride_invA * previous_batch_count - ldinvA = 128 - batch_count = 1 @param[in] handle [rocblas_handle] handle to the rocblas library context queue. @param[in] side [rocblas_side] rocblas_side_left: op(A)*X = alpha*B. rocblas_side_right: X*op(A) = alpha*B. @param[in] uplo [rocblas_fill] rocblas_fill_upper: each A_i is an upper triangular matrix. rocblas_fill_lower: each A_i is a lower triangular matrix. @param[in] transA [rocblas_operation] transB: op(A) = A. rocblas_operation_transpose: op(A) = A^T. rocblas_operation_conjugate_transpose: op(A) = A^H. @param[in] diag [rocblas_diagonal] rocblas_diagonal_unit: each A_i is assumed to be unit triangular. rocblas_diagonal_non_unit: each A_i is not assumed to be unit triangular. @param[in] m [rocblas_int] m specifies the number of rows of each B_i. m >= 0. @param[in] n [rocblas_int] n specifies the number of columns of each B_i. n >= 0. @param[in] alpha [void *] device pointer or host pointer specifying the scalar alpha. When alpha is &zero then A is not referenced, and B need not be set before entry. @param[in] A [void *] device pointer storing matrix A. of dimension ( lda, k ), where k is m when rocblas_side_left and is n when rocblas_side_right only the upper/lower triangular part is accessed. @param[in] lda [rocblas_int] lda specifies the first dimension of A. if side = rocblas_side_left, lda >= max( 1, m ), if side = rocblas_side_right, lda >= max( 1, n ). @param[in] stride_A [rocblas_stride] The stride between each A matrix. @param[in, out] B [void *] device pointer pointing to first matrix B_i. each B_i is of dimension ( ldb, n ). Before entry, the leading m by n part of each array B_i must contain the right-hand side of matrix B_i, and on exit is overwritten by the solution matrix X_i. @param[in] ldb [rocblas_int] ldb specifies the first dimension of each B_i. ldb >= max( 1, m ). @param[in] stride_B [rocblas_stride] The stride between each B_i matrix. @param[in] batch_count [rocblas_int] specifies how many batches. @param[in] invA [void *] device pointer storing the inverse diagonal blocks of each A_i. invA points to the first invA_1. each invA_i is of dimension ( ld_invA, k ), where k is m when rocblas_side_left and is n when rocblas_side_right. ld_invA must be equal to 128. @param[in] invA_size [rocblas_int] invA_size specifies the number of elements of device memory in each invA_i. @param[in] stride_invA [rocblas_stride] The stride between each invA matrix. @param[in] compute_type [rocblas_datatype] specifies the datatype of computation ********************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_trsm_strided_batched_ex(rocblas_handle handle, rocblas_side side, rocblas_fill uplo, rocblas_operation transA, rocblas_diagonal diag, rocblas_int m, rocblas_int n, const void* alpha, const void* A, rocblas_int lda, rocblas_stride stride_A, void* B, rocblas_int ldb, rocblas_stride stride_B, rocblas_int batch_count, const void* invA, rocblas_int invA_size, rocblas_stride stride_invA, rocblas_datatype compute_type); /*! BLAS Auxiliary API \details rocblas_status_to_string Returns string representing rocblas_status value @param[in] status [rocblas_status] rocBLAS status to convert to string */ ROCBLAS_EXPORT const char* rocblas_status_to_string(rocblas_status status); /* * =========================================================================== * build information * =========================================================================== */ /*! \brief loads char* buf with the rocblas library version. size_t len is the maximum length of char* buf. \details @param[in, out] buf pointer to buffer for version string @param[in] len length of buf ******************************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_get_version_string(char* buf, size_t len); /* * =========================================================================== * device memory allocation * =========================================================================== */ /*! \brief \details Indicates that subsequent rocBLAS kernel calls should collect the optimal device memory size in bytes for their given kernel arguments, and keep track of the maximum. Each kernel call can reuse temporary device memory on the same stream, so the maximum is collected. Returns rocblas_status_size_query_mismatch if another size query is already in progress; returns rocblas_status_success otherwise. @param[in] handle rocblas handle ******************************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_start_device_memory_size_query(rocblas_handle handle); /*! \brief \details Stops collecting optimal device memory size information Returns rocblas_status_size_query_mismatch if a collection is not underway; rocblas_status_invalid_handle if handle is nullptr; rocblas_status_invalid_pointer if size is nullptr; rocblas_status_success otherwise @param[in] handle rocblas handle @param[out] size maximum of the optimal sizes collected ******************************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_stop_device_memory_size_query(rocblas_handle handle, size_t* size); /*! \brief \details Gets the current device memory size for the handle Returns rocblas_status_invalid_handle if handle is nullptr; rocblas_status_invalid_pointer if size is nullptr; rocblas_status_success otherwise @param[in] handle rocblas handle @param[out] size current device memory size for the handle ******************************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_get_device_memory_size(rocblas_handle handle, size_t* size); /*! \brief \details Changes the size of allocated device memory at runtime. Any previously allocated device memory is freed. If size > 0 sets the device memory size to the specified size (in bytes) If size == 0 frees the memory allocated so far, and lets rocBLAS manage device memory in the future, expanding it when necessary Returns rocblas_status_invalid_handle if handle is nullptr; rocblas_status_invalid_pointer if size is nullptr; rocblas_status_success otherwise @param[in] handle rocblas handle @param[in] size size of allocated device memory ******************************************************************************/ ROCBLAS_EXPORT rocblas_status rocblas_set_device_memory_size(rocblas_handle handle, size_t size); /*! \brief \details Returns true when device memory in handle is managed by rocBLAS @param[in] handle rocblas handle ******************************************************************************/ ROCBLAS_EXPORT bool rocblas_is_managing_device_memory(rocblas_handle handle); #ifdef __cplusplus } #endif #endif /* _ROCBLAS_FUNCTIONS_H_ */