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：

1. C++ array
2. C++ STL vector
3. C++ thrust(CPU)
4. C++ thrust(GPU)
5. python
6. python numpy

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：

Intel Xeon CPU [email protected] x24

VS2010

python 2.7.6 (32bit)

thrust v1.5

numpy 1.8.1

---

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_rownum=2000;
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++ thrust(GPU)

创建全0向量：0.140s

 

int vect_size=1000000;

thrust::device_vector<float> vector1(vect_size);

 

创建+填充向量：0.140s

 

 

int vect_size=1000000;

thrust::device_vector<float> vector1(vect_size,0.1);

 

CPU向量赋值：0.141s

int vect_size=1000000;

thrust::host_vector<float> vector1(vect_size,0.1);

start_t=clock();

thrust::device_vector<float> vector2=vector1;

end_t=clock();

填充向量：0.000s

int vect_size=1000000;

thrust::device_vector<float> vector(vect_size);

start_t=clock();

thrust::fill(vector.begin(),vector.end(),(float)0.1);

end_t=clock();

向量点乘：0.016s

int vect_size=100000000;

thrust::device_vector<float> vector1(vect_size,(float)0.1);

thrust::device_vector<float> vector2(vect_size,(float)0.2);

thrust::device_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：0.000s

int vect_size=100000000;

thrust::device_vector<float> vector1(vect_size,(float)0.1);

thrust::device_vector<float> vector2(vect_size,(float)0.2);

thrust::device_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（实现1）：0.530s

int matrix1_rownum=2000;

int matrix1_colnum=50000;

int matrix1_size=matrix1_colnum*matrix1_rownum;

 

thrust::device_vector<float> vector1(matrix1_size,(float)0.1);

thrust::device_vector<float> vector2(matrix1_colnum,(float)0.2);

thrust::device_vector<float> tmp(matrix1_colnum);

thrust::device_vector<float> vector3(matrix1_rownum);

 

start_t=clock();

for(int row=0;row<matrix1_rownum;++row){

	thrust::transform(vector1.begin()+row*matrix1_colnum,vector1.begin()+(row+1)*matrix1_colnum,vector2.begin(),tmp.begin(),thrust::multiplies<float>());

	vector3[row]=thrust::reduce(tmp.begin(),tmp.end(),(float)0,thrust::multiplies<float>());

}

end_t=clock();

矩阵乘向量（实现2）CUBLAS，待试

矩阵乘矩阵CUBLAS，待试

 

Python

直接使用python的list实现上述功能实在太慢……而且由于无法指定float类型，其默认使用16位double类型来表示小数，使用10^8会超出list索引上限……故只使用10^7实验，速度差距可以自行换算。

大致估算python的向量运算比c++慢50倍，矩阵运算慢1000。

初始化向量并赋值：1.51s

vector_size=10000000

vector=[]

for i in range(vector_size):

	vector.append(0.1)

向量点乘:1.75s

vector_size=10000000
vector1=[]

for i in range(vector_size):

	vector1.append(0.1)

vector2=[]

for i in range(vector_size):

	vector2.append(0.1)

start_t=time.time()

sum=0

for i in range(vector_size):

	sum+=vector1[i]*vector2[i]

end_t=time.time()

向量相乘：2.39

vector_size=10000000

vector1=[]

for i in range(vector_size):

	vector1.append(0.1)

vector2=[]

for i in range(vector_size):

	vector2.append(0.1)

vector3=[]

for i in range(vector_size):

	vector3.append(0.1)

start_t=time.time()

for i in range(vector_size):

	vector3[i]=vector1[i]*vector2[i]

end_t=time.time()

矩阵乘向量：3.06s

matrix1_rownum=2000

matrix1_colnum=5000

matrix1_size=matrix1_rownum*matrix1_colnum

vector1=[]

for i in range(matrix1_size):

	vector1.append(0.1)

vector2=[]

for i in range(matrix1_colnum):

	vector2.append(0.1)

vector3=[]

for i in range(matrix1_rownum):

	vector3.append(0.1)

start_t=time.time()

for row in range(matrix1_rownum):

	for col in range(matrix1_colnum):

		vector3[row]=vector1[row*matrix1_colnum+col]*vector2[col]

end_t=time.time()

矩阵相乘：11.37s

matrix1_rownum=200

matrix1_colnum=500

matrix1_size=matrix1_rownum*matrix1_colnum

vector1=[]

for i in range(matrix1_size):

	vector1.append(0.1)

matrix2_rownum=500

matrix2_colnum=200

matrix2_size=matrix2_rownum*matrix2_colnum

vector2=[]

for i in range(matrix2_size):

	vector2.append(0.1)

matrix3_size=matrix1_rownum*matrix2_colnum

vector3=[]

for i in range(matrix3_size):

	vector3.append(0.1)

start_t=time.time()

for row in range(matrix1_rownum):

	for col in range(matrix2_colnum):

		for i in range(matrix1_colnum):

			vector3[row*matrix2_colnum+col]+=vector1[row*matrix1_colnum+i]*vector2[i*matrix2_colnum+col]

end_t=time.time()

当然实际进行向量运算没人会拿python的list数据结构进行运算，这里只是好奇定量测一下list到底有多慢……

Python numpy

创建全0向量：0.0s

vector_size=100000000

vector=numpy.zeros(vector_size)

创建+填充向量：0.25s

vector_size=100000000

vector=numpy.zeros(vector_size)

vector.fill(0.01)

向量点乘：0.125s(由于python是32位……内存原因，数据规模减半)

vector_size=50000000

vector1=numpy.zeros(vector_size)

vector1.fill(0.01)

vector2=numpy.zeros(vector_size)

vector2.fill(0.02)

start_t=time.time()

sum=numpy.inner(vector1,vector2)

end_t=time.time()

向量相乘：0.234s

vector_size=50000000

vector1=numpy.zeros(vector_size)

vector1.fill(0.01)

vector2=numpy.zeros(vector_size)

vector2.fill(0.02)

start_t=time.time()

vector3=numpy.multiply(vector1,vector2)

end_t=time.time()

矩阵乘向量：0.094s

matrix1_rownum=2000

matrix1_colnum=50000

matrix1_size=matrix1_rownum*matrix1_colnum

vector1=numpy.zeros(matrix1_size)

vector1.fill(0.01)



vector2=numpy.zeros(matrix1_colnum)

vector2.fill(0.02)



start_t=time.time()

vector1=vector1.reshape(matrix1_rownum,matrix1_colnum)

vector2=vector2.reshape(matrix1_colnum,1)

vector3=numpy.dot(vector1,vector2)

end_t=time.time()

矩阵乘矩阵：23.16s（numpy.dot出乎意料的慢，使用numpy.matrix类时间为11.73s，依旧很慢而且占用更大内存，在创建matrix对象时也要0.4s）

matrix1_rownum=2000

matrix1_colnum=50000

matrix1_size=matrix1_rownum*matrix1_colnum

vector1=numpy.zeros(matrix1_size)

vector1.fill(0.01)

matrix2_rownum=50000

matrix2_colnum=1000

matrix2_size=matrix2_rownum*matrix2_colnum

vector2=numpy.zeros(matrix2_size)

vector2.fill(0.02)

start_t=time.time()

vector1=vector1.reshape(matrix1_rownum,matrix1_colnum)

vector2=vector2.reshape(matrix2_rownum,matrix2_colnum)

vector3=numpy.dot(vector1,vector2)

end_t=time.time()