[英]why blas is slower than numpy
感謝Mats Petersson的幫助。 他的C ++運行時間最終看起來不錯! 但我有兩個新問題。
Mats Petersson的C ++代碼是:
#include <iostream>
#include <openblas/cblas.h>
#include <array>
#include <iterator>
#include <random>
#include <ctime>
using namespace std;
const blasint m = 100, k = 100, n = 100;
// Mats Petersson's declaration
array<array<double, k>, m> AA[500];
array<array<double, n>, k> BB[500];
array<array<double, n>, m> CC[500];
// My declaration
array<array<double, k>, m> AA1;
array<array<double, n>, k> BB1;
array<array<double, n>, m> CC1;
int main(void) {
CBLAS_ORDER Order = CblasRowMajor;
CBLAS_TRANSPOSE TransA = CblasNoTrans, TransB = CblasNoTrans;
const float alpha = 1;
const float beta = 0;
const int lda = k;
const int ldb = n;
const int ldc = n;
default_random_engine r_engine(time(0));
uniform_real_distribution<double> uniform(0, 1);
double dur = 0;
clock_t start,end;
double total = 0;
// Mats Petersson's initialization and computation
for(int i = 0; i < 500; i++) {
for (array<array<double, k>, m>::iterator iter = AA[i].begin(); iter != AA[i].end(); ++iter) {
for (double &number : (*iter))
number = uniform(r_engine);
}
for (array<array<double, n>, k>::iterator iter = BB[i].begin(); iter != BB[i].end(); ++iter) {
for (double &number : (*iter))
number = uniform(r_engine);
}
}
start = clock();
for(int i = 0; i < 500; ++i){
cblas_dgemm(Order, TransA, TransB, m, n, k, alpha, &AA[i][0][0], lda, &BB[i][0][0], ldb, beta, &CC[i][0][0], ldc);
}
end = clock();
dur += (double)(end - start);
cout<<endl<<"Mats Petersson spends "<<(dur/CLOCKS_PER_SEC)<<" seconds to compute it"<<endl<<endl;
// It turns me!
dur = 0;
for(int i = 0; i < 500; i++){
for(array<array<double, k>, m>::iterator iter = AA1.begin(); iter != AA1.end(); ++iter){
for(double& number : (*iter))
number = uniform(r_engine);
}
for(array<array<double, n>, k>::iterator iter = BB1.begin(); iter != BB1.end(); ++iter){
for(double& number : (*iter))
number = uniform(r_engine);
}
start = clock();
cblas_dgemm(Order, TransA, TransB, m, n, k, alpha, &AA1[0][0], lda, &BB1[0][0], ldb, beta, &CC1[0][0], ldc);
end = clock();
dur += (double)(end - start);
}
cout<<endl<<"I spend "<<(dur/CLOCKS_PER_SEC)<<" seconds to compute it"<<endl<<endl;
}
結果如下:
Mats Petersson spends 0.215056 seconds to compute it
I spend 0.459066 seconds to compute it
那么,為什么他的代碼比我的代碼快兩倍呢?
numpy代碼是
import numpy as np
import time
a = {}
b = {}
c = {}
for i in range(500):
a[i] = np.matrix(np.random.rand(100, 100))
b[i] = np.matrix(np.random.rand(100, 100))
c[i] = np.matrix(np.random.rand(100, 100))
start = time.time()
for i in range(500):
c[i] = a[i]*b[i]
print(time.time() - start)
仍然無法理解!
因此,我無法使用此代碼重現原始結果:
#include <iostream>
#include <openblas/cblas.h>
#include <array>
#include <iterator>
#include <random>
#include <ctime>
using namespace std;
const blasint m = 100, k = 100, n = 100;
array<array<double, k>, m> AA[500];
array<array<double, n>, k> BB[500];
array<array<double, n>, m> CC[500];
int main(void) {
CBLAS_ORDER Order = CblasRowMajor;
CBLAS_TRANSPOSE TransA = CblasNoTrans, TransB = CblasNoTrans;
const float alpha = 1;
const float beta = 0;
const int lda = k;
const int ldb = n;
const int ldc = n;
default_random_engine r_engine(time(0));
uniform_real_distribution<double> uniform(0, 1);
double dur = 0;
clock_t start,end;
double total = 0;
for(int i = 0; i < 500; i++){
for(array<array<double, k>, m>::iterator iter = AA[i].begin(); iter != AA[i].end(); ++iter){
for(double& number : (*iter))
number = uniform(r_engine);
}
for(array<array<double, n>, k>::iterator iter = BB[i].begin(); iter != BB[i].end(); ++iter){
for(double& number : (*iter))
number = uniform(r_engine);
}
}
start = clock();
for(int i = 0; i < 500; i++)
{
cblas_dgemm(Order, TransA, TransB, m, n, k, alpha, &AA[i][0][0], lda, &BB[i][0][0], ldb, beta,
&CC[i][0][0], ldc);
total += CC[i][i/5][i/5];
}
end = clock();
dur = (double)(end - start);
cout<<endl<<"It spends "<<(dur/CLOCKS_PER_SEC)<<" seconds to compute it"<<endl<<endl;
cout << "total =" << total << endl;
}
而這段代碼:
import numpy as np
import time
a = {}
b = {}
c = {}
for i in range(500):
a[i] = np.matrix(np.random.rand(100, 100))
b[i] = np.matrix(np.random.rand(100, 100))
c[i] = np.matrix(np.random.rand(100, 100))
start = time.time()
for i in range(500):
c[i] = a[i]*b[i]
print(time.time() - start)
我們知道循環(幾乎)做同樣的事情。 我的結果如下:
使數組全局化可確保我們不會炸毀堆棧。 我還將rengine1更改為rengine,因為它不會像以前那樣編譯。
然后我確保兩個示例計算500個不同的數組值。
有趣的是,g ++的總時間比clang ++的總時間短得多 - 但這是時間測量之外的循環,實際的矩陣乘法是相同的,給出或取千分之一秒。 python的總執行時間介於clang和g ++之間。
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