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使用blas做矩阵乘法

2024-07-14 08:02:16   232人阅读

 
#define min(x,y) (((x) < (y)) ? (x) : (y))#include <stdio.h>#include <stdlib.h>#include <cublas_v2.h>#include <iostream>#include <vector>//extern "C"//{   #include <cblas.h>//}using namespace std;int main(){    const enum CBLAS_ORDER Order=CblasRowMajor;    const enum CBLAS_TRANSPOSE TransA=CblasNoTrans;    const enum CBLAS_TRANSPOSE TransB=CblasNoTrans;    const int M=4;//A的行数，C的行数    const int N=2;//B的列数，C的列数    const int K=3;//A的列数，B的行数    const float alpha=1;    const float beta=0;    const int lda=K;//A的列    const int ldb=N;//B的列    const int ldc=N;//C的列    const float A[M*K]={1,2,3,4,5,6,7,8,9,8,7,6};    const float B[K*N]={5,4,3,2,1,0};    float C[M*N];       cblas_sgemm(Order, TransA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc);         for(int i=0;i<M;i++)    {       for(int j=0;j<N;j++)       {           cout<<C[i*N+j]<<"\n";       }       cout<<endl;    }       return EXIT_SUCCESS; }

g++ testblas.c++ -lopenblas  -o testout

g++ testblas.c++ -lopenblas_piledriverp-r0.2.9 -o testout   本地编译openblas版本

注意library放在引用library的函数的后面

cblas_sgemmMultiplies two matrices (single-precision).void cblas_sgemm (const enum CBLAS_ORDER Order,  // Specifies row-major (C) or column-major (Fortran) data ordering.//typedef enum CBLAS_ORDER     {CblasRowMajor=101, CblasColMajor=102} CBLAS_ORDER;const enum CBLAS_TRANSPOSE TransA,//Specifies whether to transpose matrix A.const enum CBLAS_TRANSPOSE TransB,const int M,   //Number of rows in matrices A and C.const int N,//Number of rows in matrices A and C.const int K,  //Number of columns in matrix A; number of rows in matrix Bconst float alpha, //Scaling factor for the product of matrices A and Bconst float *A, const int lda, //The size of the first dimention of matrix A; if you are passing a matrix A[m][n], the value should be m.const float *B,  const int ldb,  //The size of the first dimention of matrix B; if you are passing a matrix B[m][n], the value should be m.const float beta,  //Scaling factor for matrix C.float *C,const int ldc    //The size of the first dimention of matrix C; if you are passing a matrix C[m][n], the value should be m.);Thus, it calculates eitherC←αAB + βCorC←αBA + βCwith optional use of transposed forms of A, B, or both.
 
typedef enum CBLAS_ORDER     {CblasRowMajor=101, CblasColMajor=102} CBLAS_ORDER;typedef enum CBLAS_TRANSPOSE {CblasNoTrans=111, CblasTrans=112, CblasConjTrans=113, CblasConjNoTrans=114} CBLAS_TRANSPOSE;
$C=A*B$
$C^T=(A*B)^T=B^T*A^T$  把A和B的顺序颠倒，可以直接得到转制矩阵乘法的结果，不用作其他变换，（结果C也是转制）。
 
 
cblas_sgemvMultiplies a matrix by a vector (single precision).
void cblas_sgemv (const enum CBLAS_ORDER Order,const enum CBLAS_TRANSPOSE TransA,const int M,const int N,const float alpha,const float *A,const int lda,const float *X,const int incX,const float beta,float *Y,const int incY);
Y←αAX + βY
 
STL版本
cblas_daxpy
Computes a constant times a vector plus a vector (double-precision).　　
On return, the contents of vector Y are replaced with the result. The value computed is (alpha * X[i]) +
Y[i].
#include <OpenBlas/cblas.h>#include <OpenBlas/common.h>#include <iostream>#include <vector>int main(){    blasint n = 10;    blasint in_x =1;    blasint in_y =1;    std::vector<double> x(n);    std::vector<double> y(n);    double alpha = 10;    std::fill(x.begin(),x.end(),1.0);    std::fill(y.begin(),y.end(),2.0);    cblas_daxpy( n, alpha, &x[0], in_x, &y[0], in_y);    //Print y     for(int j=0;j<n;j++)        std::cout << y[j] << "\t";    std::cout << std::endl;}
 






























        

            
                
                  
                
                

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