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C++ vs Python向量运算速度评测
本文的起源来自最近一个让我非常不爽的事。
我最近在改一个开源RNN工具包currennt(http://sourceforge.net/projects/currennt/),想用它实现RNNLM功能。
currennt使用了大量的面向对象的编程技巧,可以使用GPU,向量运算使用了thrust库(https://code.google.com/p/thrust/)。
RNNLM(http://rnnlm.org/)也有相应开源实现,非常算法风格的代码,向量运算就是自己使用数组实现的。
结果……大出我的语料,在不使用GPU的情况下,currennt慢成狗!我不断的修改,直到最后几乎完全在currennt里重写了一个RNNLM……速度才终于一致了。这花费了我大量时间,最关键的是我根本没打算花这些时间,算是计划外开销。
所以这里干脆对常用的几种向量运算做个评测,下回遇到至少心里有数。
参与评测的向量实现包括:
- C++ array
- C++ STL vector
- C++ thrust(CPU)
- C++ thrust(GPU)
- python
- python numpy
- python theano
评测指标包括:
- 创建、填充向量
- 向量点乘,相乘
- 矩阵相乘
测试环境:
VS2010
python 2.7.6
Intel Xeon CPU E5649@2.53GHz x24
thrust v1.5
C++ array
创建全0向量:0.000s,几乎不占用时间
int vector_size=100000000;float* vector=(float*)calloc(vector_size,sizeof(float));
创建+填充向量:0.140s
int vector_size=100000000;float* vector=(float*)calloc(vector_size,sizeof(float));for (int i=0;i<vector_size;++i){ vector[i]=0.01;}
向量点乘:0.390s
float sum=0;for(int i=0;i<vector_size;++i){ sum+=vector1[i]*vector2[i];}
向量相乘:0.265s
float sum=0;for(int i=0;i<vector_size;++i){ vector3[i]=vector1[i]*vector2[i];}
矩阵乘向量:0.344s
int matrix1_colnum=50000;int matrix1_rownum=2000;int matrix1_size=matrix1_colnum*matrix1_rownum;float* vector1=(float*)calloc(matrix1_size,sizeof(float));for (int i=0;i<matrix1_size;++i){ vector1[i]=0.01;}float* vector2=(float*)calloc(matrix1_colnum,sizeof(float));for (int i=0;i<matrix1_colnum;++i){ vector2[i]=0.02;}start_t=clock();float* vector3=(float*)calloc(matrix1_rownum,sizeof(float));for(int row=0;row<matrix1_rownum;++row){ for(int col=0;col<matrix1_colnum;++col){ vector3[row]+=vector1[row*matrix1_colnum+col]*vector2[col]; }}end_t=clock();
矩阵乘矩阵:0.749
(耗费时间与matrix1_rownum*matrix1_colnum*matrix2_colnum成正比)
int matrix1_rownum=200;int matrix1_colnum=5000;int matrix1_size=matrix1_colnum*matrix1_rownum;float* vector1=(float*)calloc(matrix1_size,sizeof(float));for (int i=0;i<matrix1_size;++i){ vector1[i]=0.01;}int matrix2_rownum=5000;int matrix2_colnum=200;int matrix2_size=matrix2_rownum*matrix2_colnum;float* vector2=(float*)calloc(matrix2_size,sizeof(float));for (int i=0;i<matrix2_size;++i){ vector2[i]=0.02;}int matrix3_size=matrix1_rownum*matrix2_colnum;float* vector3=(float*)calloc(matrix3_size,sizeof(float));start_t=clock();for(int row1=0;row1<matrix1_rownum;++row1){ for(int col2=0;col2<matrix2_colnum;++col2){ for(int col1=0;col1<matrix1_colnum;++col1){ vector3[row1*matrix2_colnum+col2]+=vector1[row1*matrix1_colnum+col1]*vector2[col1*matrix2_colnum+col2]; } }}end_t=clock();
C++ STL vector
创建全0向量:0.140s
int vect_size=100000000;
vector<float> vector(vect_size);
创建+填充向量:0.140s
int vect_size=100000000;vector<float> vector(vect_size,0.01);
向量点乘:0.375s
int vect_size=100000000;vector<float> vector1(vect_size,0.01);vector<float> vector2(vect_size,0.02);start_t=clock();float sum=0;for(int i=0;i<vect_size;++i){ sum+=vector1[i]*vector2[i];}end_t=clock();
向量相乘:0.250s
int vect_size=100000000;vector<float> vector1(vect_size,0.01);vector<float> vector2(vect_size,0.02);vector<float> vector3(vect_size);start_t=clock();for(int i=0;i<vect_size;++i){ vector3[i]=vector1[i]*vector2[i];}end_t=clock();
矩阵乘向量:0.390s
int matrix1_colnum=50000;int matrix1_rownum=2000;int matrix1_size=matrix1_colnum*matrix1_rownum;vector<float> vector1(matrix1_size,0.01);vector<float> vector2(matrix1_colnum,0.02);vector<float> vector3(matrix1_rownum);start_t=clock();for(int row=0;row<matrix1_rownum;++row){ for(int col=0;col<matrix1_colnum;++col){ vector3[row]+=vector1[row*matrix1_colnum+col]*vector2[col]; }}end_t=clock();
矩阵乘法:0.827s
int matrix1_rownum=200;int matrix1_colnum=5000;int matrix1_size=matrix1_colnum*matrix1_rownum;vector<float> vector1(matrix1_size,0.01);int matrix2_rownum=5000;int matrix2_colnum=200;int matrix2_size=matrix2_rownum*matrix2_colnum;vector<float> vector2(matrix2_size,0.02);int matrix3_size=matrix1_rownum*matrix2_colnum;vector<float> vector3(matrix3_size);start_t=clock();for(int row1=0;row1<matrix1_rownum;++row1){ for(int col2=0;col2<matrix2_colnum;++col2){ for(int col1=0;col1<matrix1_colnum;++col1){ vector3[row1*matrix2_colnum+col2]+=vector1[row1*matrix1_colnum+col1]*vector2[col1*matrix2_colnum+col2]; } }}end_t=clock();
C++ thrust(CPU)
创建全0向量:0.140s
int vect_size=100000000;thrust::host_vector<float> vector1(vect_size);
创建+填充向量:0.140s
int vect_size=100000000;thrust::host_vector<float> vector1(vect_size,0.01);
填充向量:0.078s
thrust::fill(vector1.begin(),vector1.end(),0.01);
向量点乘:0.359s
int vect_size=100000000;thrust::host_vector<float> vector1(vect_size,(float)0.1);thrust::host_vector<float> vector2(vect_size,(float)0.2);thrust::host_vector<float> vector3(vect_size,(float)0.2);start_t=clock();thrust::transform(vector1.begin(),vector1.end(),vector2.begin(),vector3.begin(),thrust::multiplies<float>());float sum=thrust::reduce(vector3.begin(),vector3.end(),(float)0,thrust::multiplies<float>());end_t=clock();
向量相乘:0.187s
int vect_size=100000000;thrust::host_vector<float> vector1(vect_size,(float)0.1);thrust::host_vector<float> vector2(vect_size,(float)0.2);thrust::host_vector<float> vector3(vect_size);start_t=clock();thrust::transform(vector1.begin(),vector1.end(),vector2.begin(),vector3.begin(),thrust::multiplies<float>());end_t=clock();
矩阵乘向量:0.110s
struct matrixXvect_func{ thrust::host_vector<float>* matrix; thrust::host_vector<float>* vector; int matrix_rownum; int matrix_colnum; __host__ __device__ float operator()(const int& idx) const{ float t=0; for(int col=0;col<matrix_colnum;++col){ t+=(*matrix)[idx*matrix_colnum+col]* (*vector)[col]; } return t; }};int matrix1_colnum=50000;int matrix1_size=matrix1_colnum*matrix1_rownum;thrust::host_vector<float> vector1(matrix1_size,(float)0.1);thrust::host_vector<float> vector2(matrix1_colnum,(float)0.2);thrust::host_vector<float> vector3(matrix1_rownum);start_t=clock();matrixXvect_func fn;fn.matrix=&vector1;fn.vector=&vector2;fn.matrix_rownum=matrix1_rownum;fn.matrix_colnum=matrix1_colnum;thrust::transform( thrust::counting_iterator<int>(0), thrust::counting_iterator<int>(0) + matrix1_rownum, vector3.begin(), fn );end_t=clock();
矩阵乘矩阵:0.655s
struct matrixXmatrix_func{ thrust::host_vector<float>* matrix1; thrust::host_vector<float>* matrix2; int matrix1_rownum; int matrix1_colnum; int matrix2_rownum; int matrix2_colnum; __host__ __device__ float operator()(const int& idx) const{ int rownum=idx/matrix2_colnum; int colnum=idx%matrix2_colnum; float t=0; for(int col=0;col<matrix1_colnum;++col){ t+=(*matrix1)[rownum*matrix1_colnum+col]* (*matrix2)[col*matrix2_colnum+colnum]; } return t; }};int matrix1_rownum=200;int matrix1_colnum=5000;int matrix1_size=matrix1_colnum*matrix1_rownum;thrust::host_vector<float> vector1(matrix1_size,(float)0.1);int matrix2_rownum=5000;int matrix2_colnum=200;int matrix2_size=matrix2_rownum*matrix2_colnum;thrust::host_vector<float> vector2(matrix2_size,(float)0.2);int matrix3_size=matrix1_rownum*matrix2_colnum;thrust::host_vector<float> vector3(matrix3_size);start_t=clock();matrixXmatrix_func fn;fn.matrix1=&vector1;fn.matrix2=&vector2;fn.matrix1_rownum=matrix1_rownum;fn.matrix1_colnum=matrix1_colnum;fn.matrix2_rownum=matrix2_rownum;fn.matrix2_colnum=matrix2_colnum;thrust::transform( thrust::counting_iterator<int>(0), thrust::counting_iterator<int>(0) + matrix3_size, vector3.begin(), fn );end_t=clock();
C++ vs Python向量运算速度评测