如何优化矩阵乘法运算 [英] How to optimize matrix multiplication operation

问题描述

我需要在我的应用程序中执行大量的矩阵操作。最耗时的是矩阵乘法。我以这种方式实现

I need to perform a lot of matrix operations in my application. The most time consuming is matrix multiplication. I implemented it this way

template<typename T>
Matrix<T> Matrix<T>::operator * (Matrix& matrix)
{


    Matrix<T> multipliedMatrix = Matrix<T>(this->rows,matrix.GetColumns(),0);

    for (int i=0;i<this->rows;i++)
    {
        for (int j=0;j<matrix.GetColumns();j++)
        {
            multipliedMatrix.datavector.at(i).at(j) = 0;
            for (int k=0;k<this->columns ;k++)
            {
                multipliedMatrix.datavector.at(i).at(j) +=  datavector.at(i).at(k) * matrix.datavector.at(k).at(j);
            }
            //cout<<(*multipliedMatrix)[i][j]<<endl;
        }
    }
    return multipliedMatrix;
}

有什么办法写得更好吗？到目前为止矩阵乘法运算占用我的应用程序的大部分时间。也许有好/快速图书馆做这种东西吗？
但是我宁可不使用它使用的图形卡进行数学运算，因为事实上，我与集成显卡的笔记本电脑工作的库。

Is there any way to write it in a better way?? So far matrix multiplication operations take most of time in my application. Maybe is there good/fast library for doing this kind of stuff ?? However I rather can't use libraries which uses graphic card for mathematical operations, because of the fact that I work on laptop with integrated graphic card.

推荐答案

征是目前为止最快的国家之一，如果不是最快的，线性代数库。它是写得很好，它是高质量的。此外，它使用表达式模板，这使得编写代码更加可读。刚刚发布的版本3使用OpenMP进行数据并行。

Eigen is by far one of the fastest, if not the fastest, linear algebra libraries out there. It is well written and it is of high quality. Also, it uses expression template which makes writing code that is more readable. Version 3 just released uses OpenMP for data parallelism.

#include <iostream>
#include <Eigen/Dense>

using Eigen::MatrixXd;

int main()
{
  MatrixXd m(2,2);
  m(0,0) = 3;
  m(1,0) = 2.5;
  m(0,1) = -1;
  m(1,1) = m(1,0) + m(0,1);
  std::cout << m << std::endl;
}

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