给定两个矩阵A
和B
,以及C
作为它们相乘的结果。
#include "boost/multi_array.hpp"
typedef boost::multi_array<double, 2> matrix;
int m=5;
int n=6;
int k=7;
matrix A(boost::extents[m][k]);
matrix B(boost::extents[k][n]);
matrix C(boost::extents[m][n]);
如何调用 blas
库中的 dgemm
函数来计算 A
和 B
的矩阵乘积?
我知道 uBLAS
部分 boost
库、armadillo
、MTL 4
、eigen
以及其他一些为 blas 函数提供方便的包装器的库。这里的问题是如何直接在多数组上调用dgemm
。
最佳答案
您可以访问连续元素存储。
原型(prototype)是
void cblas_dgemm( CBLAS_LAYOUT layout, CBLAS_TRANSPOSE TransA, CBLAS_TRANSPOSE TransB, const int M, const int N, const int K, const double alpha, const double *A, const int lda, const double *B, const int ldb, const double beta, double *C, const int ldc )
那么,让我们填写一下:
cblas_dgemm(
CBLAS_LAYOUT::CblasRowMajor,
CBLAS_TRANSPOSE::CblasNoTrans,
CBLAS_TRANSPOSE::CblasNoTrans,
m, n, k,
1.0, // alpha
A.data(), A.shape()[1],
B.data(), B.shape()[1],
0.0, // beta
C.data(), C.shape()[1]);
That was using the docs here for a bit of guidance on alpha/beta:
演示
#include <boost/multi_array.hpp>
typedef boost::multi_array<double, 2> matrix;
#include <iostream>
namespace io { // for debug output
auto& dump(std::ostream& os, double v) { return os << v; }
template <typename R> auto& dump(std::ostream& os, R const& r) {
std::string_view sep = "";
os << "{";
for (auto const& el : r) { dump(os << sep, el); sep = ","; }
return os << "}";
}
}
std::ostream& operator<<(std::ostream& os, matrix const& m) { return io::dump(os, m); }
// demo
#include <cblas-netlib.h>
#include <numeric> // iota
int main() {
constexpr auto m=5, n=6, k=7;
matrix A(boost::extents[m][k]);
matrix B(boost::extents[k][n]);
matrix C(boost::extents[m][n]);
std::iota(A.data(), A.data() + A.num_elements(), 0);
std::iota(B.data(), B.data() + B.num_elements(), 50);
std::iota(C.data(), C.data() + C.num_elements(), 100);
std::cout << "A: " << A << "\nB: " << B << "\n";
assert(A.storage_order().all_dims_ascending());
/*
* void cblas_dgemm(
* CBLAS_LAYOUT layout,
* CBLAS_TRANSPOSE TransA,
* CBLAS_TRANSPOSE TransB,
* const int M, const int N, const int K,
* const double alpha,
* const double *A, const int lda,
* const double *B, const int ldb,
* const double beta,
* double *C, const int ldc )
*/
cblas_dgemm(
CBLAS_LAYOUT::CblasRowMajor,
CBLAS_TRANSPOSE::CblasNoTrans,
CBLAS_TRANSPOSE::CblasNoTrans,
m, n, k,
1.0, // alpha
A.data(), A.shape()[1],
B.data(), B.shape()[1],
0.0, // beta
C.data(), C.shape()[1]);
std::cout << "C:\n" << C << "\n";
}
打印内容:
A: {{0,1,2,3,4,5,6},{7,8,9,10,11,12,13},{14,15,16,17,18,19,20},{21,22,23,24,25,26,27},{28,29,30,31,32,33,34}}
B: {{50,51,52,53,54,55},{56,57,58,59,60,61},{62,63,64,65,66,67},{68,69,70,71,72,73},{74,75,76,77,78,79},{80,81,82,83,84,85},{86,87,88,89,90,91}}
C:
{{1596,1617,1638,1659,1680,1701},{4928,4998,5068,5138,5208,5278},{8260,8379,8498,8617,8736,8855},{11592,11760,11928,12096,12264,12432},{14924,15141,15358,15575,15792,16009}}
这与 Wolfram Alpha 进行检查:
关于c++11 - BLAS 库如何直接与 boost multiarrays 一起使用?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/63535943/