41.cuBLAS开发指南中文版--cuBLAS中的Level-2方法gemvStridedBatched()

2.6.25. `cublasgemvStridedBatched()`

在这里插入图片描述

cublasStatus_t cublasSgemvStridedBatched(cublasHandle_t handle,
                                         cublasOperation_t trans,
                                         int m, int n,
                                         const float           *alpha,
                                         const float           *A, int lda,
                                         long long int         strideA,
                                         const float           *x, int incx,
                                         long long int         stridex,
                                         const float           *beta,
                                         float                 *y, int incy,
                                         long long int         stridey,
                                         int batchCount)
cublasStatus_t cublasDgemvStridedBatched(cublasHandle_t handle,
                                         cublasOperation_t trans,
                                         int m, int n,
                                         const double          *alpha,
                                         const double          *A, int lda,
                                         long long int         strideA,
                                         const double          *x, int incx,
                                         long long int         stridex,
                                         const double          *beta,
                                         double                *yarray[], int incy,
                                         long long int         stridey,
                                         int batchCount)
cublasStatus_t cublasCgemvStridedBatched(cublasHandle_t handle,
                                         cublasOperation_t trans,
                                         int m, int n,
                                         const cuComplex       *alpha,
                                         const cuComplex       *A, int lda,
                                         long long int         strideA,
                                         const cuComplex       *x, int incx,
                                         long long int         stridex,
                                         const cuComplex       *beta,
                                         cuComplex             *y, int incy,
                                         long long int         stridey,
                                         int batchCount)
cublasStatus_t cublasZgemvStridedBatched(cublasHandle_t handle,
                                         cublasOperation_t trans,
                                         int m, int n,
                                         const cuDoubleComplex *alpha,
                                         const cuDoubleComplex *A, int lda,
                                         long long int         strideA,
                                         const cuDoubleComplex *x, int incx,
                                         long long int         stridex,
                                         const cuDoubleComplex *beta,
                                         cuDoubleComplex       *y, int incy,
                                         long long int         stridey,
                                         int batchCount)
cublasStatus_t cublasHSHgemvStridedBatched(cublasHandle_t handle,
                                           cublasOperation_t trans,
                                           int m, int n,
                                           const float           *alpha,
                                           const __half          *A, int lda,
                                           long long int         strideA,
                                           const __half          *x, int incx,
                                           long long int         stridex,
                                           const float           *beta,
                                           __half                *y, int incy,
                                           long long int         stridey,
                                           int batchCount)
cublasStatus_t cublasHSSgemvStridedBatched(cublasHandle_t handle,
                                           cublasOperation_t trans,
                                           int m, int n,
                                           const float           *alpha,
                                           const __half          *A, int lda,
                                           long long int         strideA,
                                           const __half          *x, int incx,
                                           long long int         stridex,
                                           const float           *beta,
                                           float                 *y, int incy,
                                           long long int         stridey,
                                           int batchCount)
cublasStatus_t cublasTSTgemvStridedBatched(cublasHandle_t handle,
                                           cublasOperation_t trans,
                                           int m, int n,
                                           const float           *alpha,
                                           const __nv_bfloat16   *A, int lda,
                                           long long int         strideA,
                                           const __nv_bfloat16   *x, int incx,
                                           long long int         stridex,
                                           const float           *beta,
                                           __nv_bfloat16         *y, int incy,
                                           long long int         stridey,
                                           int batchCount)
cublasStatus_t cublasTSSgemvStridedBatched(cublasHandle_t handle,
                                           cublasOperation_t trans,
                                           int m, int n,
                                           const float           *alpha,
                                           const __nv_bfloat16   *A, int lda,
                                           long long int         strideA,
                                           const __nv_bfloat16   *x, int incx,
                                           long long int         stridex,
                                           const float           *beta,
                                           float                 *y, int incy,
                                           long long int         stridey,
                                           int batchCount)
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此函数执行一批矩阵和向量的矩阵向量乘法。 batch 被认为是“统一的”，即所有实例对于各自的 A 矩阵、x 和向量。批处理的每个实例的输入矩阵 A 和向量 x 以及输出向量 y 位于与它们在前一个实例中的位置的元素数量上的固定偏移处。用户将指向第一个实例的 A 矩阵、x 和 y 向量的指针连同元素数量的偏移量传递给函数 - strideA、stridex 和 stridey 确定输入矩阵和向量的位置，以及未来实例中的输出向量 .

$\alpha op(A+i*strideA)(x + i*stridex) + \beta (y+i*stridey), for i \in [0, batchCount -1]$

其中 $\alpha$ 和 $\beta$ 是标量, A 是指向矩阵 A[i] 的指针数组，以列优先格式存储，维度为 m x n ，x 和 y 是指向向量的指针数组。此外，对于 matrixA[i] ，

$op(A[i])= {A[i] 如果transa==CUBLAS_OP_N,A[i]T 如果transa==CUBLAS_OP_T,A[i]H 如果transa==CUBLAS_OP_C$

o p (A [i]) = ⎩ ⎪ ⎨ ⎪ ⎧ A [i] 如 果 t r a n s a = = C U B L A S_O P_N, A [i]^{T} 如 果 t r a n s a = = C U B L A S_O P_T, A [i]^{H} 如 果 t r a n s a = = C U B L A S_O P_C

注意：y[i] 向量不能重叠，也就是说，各个 gemv 操作必须是可独立计算的；否则，会出现未定义的行为。

对于某些规模的问题，在不同的 CUDA 流中多次调用 cublasgemv 可能比使用此 API 更有利。

注意：在下表中，我们使用 A[i]、x[i]、y[i] 作为 A 矩阵的符号，以及批处理的第 i 个实例中的 x 和 y 向量，隐含地假设它们分别在数字上偏移元素 strideA、stridex、stridey 远离 A[i-1]、x[i-1]、y[i-1]。偏移量的单位是元素数，不能为零。

Param.	Memory	In/out	Meaning
handle		input	handle to the cuBLAS library context.
trans		input	Operation op(A[i]) that is non- or (conj.) transpose.
m		input	Number of rows of matrix A[i].
n		input	number of columns of matrix A.
alpha	host or device	input	scalar used for multiplication.
A	device	input	`*` pointer to the A matrix corresponding to the first instance of the batch, with dimensions lda x n with lda>=max(1,m).
lda		input	Leading dimension of two-dimensional array used to store each matrix A[i].
strideA		input	Value of type long long int that gives the offset in number of elements between A[i] and A[i+1]
x	device	input	`*` pointer to the x vector corresponding to the first instance of the batch, with each dimension n if trans==CUBLAS_OP_N and m otherwise.
incx		input	stride between consecutive elements of x.
stridex		input	Value of type long long int that gives the offset in number of elements between x[i] and x[i+1]
beta	host or device	input	scalar used for multiplication. If beta == 0, y does not have to be a valid input.
y	device	in/out	`*` pointer to the y vector corresponding to the first instance of the batch, with each dimension m if trans==CUBLAS_OP_N and n otherwise. Vectors y[i] should not overlap; otherwise, undefined behavior is expected.
incy		input	Stride of each one-dimensional array y[i].
stridey		input	Value of type long long int that gives the offset in number of elements between y[i] and y[i+1]
batchCount		input	Number of pointers contained in Aarray, xarray and yarray.

该函数可能返回的错误值及其含义如下表所示：

ErrorValue	Meaning
CUBLAS_STATUS_SUCCESS	操作成功完成
CUBLAS_STATUS_NOT_INITIALIZED	库未初始化
CUBLAS_STATUS_INVALID_VALUE	参数 m,n,batchCount<0 .
CUBLAS_STATUS_EXECUTION_FAILED	该功能无法在 GPU 上启动

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原文地址：https://blog.csdn.net/kunhe0512/article/details/128143973

41.cuBLAS开发指南中文版--cuBLAS中的Level-2方法gemvStridedBatched()

2.6.25. cublasgemvStridedBatched()

2.6.25. `cublasgemvStridedBatched()`