本征矩阵与Numpy阵列乘法性能 [英] Eigen Matrix vs Numpy Array multiplication performance

问题描述

在此问题中，我阅读了 ，说eigen具有非常好的性能.但是，我试图比较eigen MatrixXi乘法速度与numpy array乘法.而且numpy的效果更好(〜26秒vs.〜29).有没有更有效的方法来执行eigen?

I read in this question that eigen has very good performance. However, I tried to compare eigen MatrixXi multiplication speed vs numpy array multiplication. And numpy performs better (~26 seconds vs. ~29). Is there a more efficient way to do this eigen?

这是我的代码:

脾气暴躁:

import numpy as np
import time

n_a_rows = 4000
n_a_cols = 3000
n_b_rows = n_a_cols
n_b_cols = 200

a = np.arange(n_a_rows * n_a_cols).reshape(n_a_rows, n_a_cols)
b = np.arange(n_b_rows * n_b_cols).reshape(n_b_rows, n_b_cols)

start = time.time()
d = np.dot(a, b)
end = time.time()

print "time taken : {}".format(end - start)

结果:

time taken : 25.9291000366

本征:

#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{

  int n_a_rows = 4000;
  int n_a_cols = 3000;
  int n_b_rows = n_a_cols;
  int n_b_cols = 200;

  MatrixXi a(n_a_rows, n_a_cols);

  for (int i = 0; i < n_a_rows; ++ i)
      for (int j = 0; j < n_a_cols; ++ j)
        a (i, j) = n_a_cols * i + j;

  MatrixXi b (n_b_rows, n_b_cols);
  for (int i = 0; i < n_b_rows; ++ i)
      for (int j = 0; j < n_b_cols; ++ j)
        b (i, j) = n_b_cols * i + j;

  MatrixXi d (n_a_rows, n_b_cols);

  clock_t begin = clock();

  d = a * b;

  clock_t end = clock();
  double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
  std::cout << "Time taken : " << elapsed_secs << std::endl;

}

结果:

Time taken : 29.05

我正在使用numpy 1.8.1和eigen 3.2.0-4.

推荐答案

更改:

a = np.arange(n_a_rows * n_a_cols).reshape(n_a_rows, n_a_cols)
b = np.arange(n_b_rows * n_b_cols).reshape(n_b_rows, n_b_cols)

进入:

a = np.arange(n_a_rows * n_a_cols).reshape(n_a_rows, n_a_cols)*1.0
b = np.arange(n_b_rows * n_b_cols).reshape(n_b_rows, n_b_cols)*1.0

这至少使我的笔记本电脑的工作效率提高了100倍:

This gives factor 100 boost at least at my laptop:

time taken : 11.1231250763

vs:

time taken : 0.124922037125

除非您真的想将整数相乘.在Eigen中，乘以双精度数也更快(相当于用MatrixXd替换MatrixXi三次)，但是我看到的只是1.5倍:花费时间:0.555005对0.846788.

Unless you really want to multiply integers. In Eigen it is also quicker to multiply double precision numbers (amounts to replacing MatrixXi with MatrixXd three times), but there I see just 1.5 factor: Time taken : 0.555005 vs 0.846788.

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