我有一个任务，需要计算数组中字节的异或和：
X = char1 XOR char2 XOR char3 ... charN;
我正在尝试并行化它，使用 __m128 进行异或运算。这样可以提高速度4倍。 此外，为了重新检查算法，我使用 int 类型。这也可以提高速度4倍。 测试程序有100行，无法缩短，但很简单:
#include "xmmintrin.h" // simulation of the SSE instruction
#include <ctime>
#include <iostream>
using namespace std;
#include <stdlib.h> // rand
const int NIter = 100;
const int N = 40000000; // matrix size. Has to be dividable by 4.
unsigned char str[N] __attribute__ ((aligned(16)));
template< typename T >
T Sum(const T* data, const int N)
{
T sum = 0;
for ( int i = 0; i < N; ++i )
sum = sum ^ data[i];
return sum;
}
template<>
__m128 Sum(const __m128* data, const int N)
{
__m128 sum = _mm_set_ps1(0);
for ( int i = 0; i < N; ++i )
sum = _mm_xor_ps(sum,data[i]);
return sum;
}
int main() {
// fill string by random values
for( int i = 0; i < N; i++ ) {
str[i] = 256 * ( double(rand()) / RAND_MAX ); // put a random value, from 0 to 255
}
/// -- CALCULATE --
/// SCALAR
unsigned char sumS = 0;
std::clock_t c_start = std::clock();
for( int ii = 0; ii < NIter; ii++ )
sumS = Sum<unsigned char>( str, N );
double tScal = 1000.0 * (std::clock()-c_start) / CLOCKS_PER_SEC;
/// SIMD
unsigned char sumV = 0;
const int m128CharLen = 4*4;
const int NV = N/m128CharLen;
c_start = std::clock();
for( int ii = 0; ii < NIter; ii++ ) {
__m128 sumVV = _mm_set_ps1(0);
sumVV = Sum<__m128>( reinterpret_cast<__m128*>(str), NV );
unsigned char *sumVS = reinterpret_cast<unsigned char*>(&sumVV);
sumV = sumVS[0];
for ( int iE = 1; iE < m128CharLen; ++iE )
sumV ^= sumVS[iE];
}
double tSIMD = 1000.0 * (std::clock()-c_start) / CLOCKS_PER_SEC;
/// SCALAR INTEGER
unsigned char sumI = 0;
const int intCharLen = 4;
const int NI = N/intCharLen;
c_start = std::clock();
for( int ii = 0; ii < NIter; ii++ ) {
int sumII = Sum<int>( reinterpret_cast<int*>(str), NI );
unsigned char *sumIS = reinterpret_cast<unsigned char*>(&sumII);
sumI = sumIS[0];
for ( int iE = 1; iE < intCharLen; ++iE )
sumI ^= sumIS[iE];
}
double tINT = 1000.0 * (std::clock()-c_start) / CLOCKS_PER_SEC;
/// -- OUTPUT --
cout << "Time scalar: " << tScal << " ms " << endl;
cout << "Time INT: " << tINT << " ms, speed up " << tScal/tINT << endl;
cout << "Time SIMD: " << tSIMD << " ms, speed up " << tScal/tSIMD << endl;
if(sumV == sumS && sumI == sumS )
std::cout << "Results are the same." << std::endl;
else
std::cout << "ERROR! Results are not the same." << std::endl;
return 1;
}
典型结果：
[10:46:20]$ g++ test.cpp -O3 -fno-tree-vectorize; ./a.out
Time scalar: 3540 ms
Time INT: 890 ms, speed up 3.97753
Time SIMD: 280 ms, speed up 12.6429
Results are the same.
[10:46:27]$ g++ test.cpp -O3 -fno-tree-vectorize; ./a.out
Time scalar: 3540 ms
Time INT: 890 ms, speed up 3.97753
Time SIMD: 280 ms, speed up 12.6429
Results are the same.
[10:46:35]$ g++ test.cpp -O3 -fno-tree-vectorize; ./a.out
Time scalar: 3640 ms
Time INT: 880 ms, speed up 4.13636
Time SIMD: 290 ms, speed up 12.5517
Results are the same.
正如您所见，int版本的效果非常理想，但是simd版本的速度损失了25%，这是稳定的。我尝试更改数组大小，但这没有帮助。
另外，如果我切换到-O2，simd版本的速度会下降75%：
[10:50:25]$ g++ test.cpp -O2 -fno-tree-vectorize; ./a.out
Time scalar: 3640 ms
Time INT: 880 ms, speed up 4.13636
Time SIMD: 890 ms, speed up 4.08989
Results are the same.
[10:51:16]$ g++ test.cpp -O2 -fno-tree-vectorize; ./a.out
Time scalar: 3640 ms
Time INT: 900 ms, speed up 4.04444
Time SIMD: 880 ms, speed up 4.13636
Results are the same.
请问有人能帮我解释一下这个吗？
额外信息：
I have g++ (GCC) 4.7.3; Intel(R) Xeon(R) CPU E7-4860
I use -fno-tree-vectorize to prevent auto vectorization. Without this flag with -O3 the expected speed up is 1, since the task is simple. This is what I get:
[10:55:40]$ g++ test.cpp -O3; ./a.out Time scalar: 270 ms Time INT: 270 ms, speed up 1 Time SIMD: 280 ms, speed up 0.964286 Results are the same.but with -O2 result is still strange:
[10:55:02]$ g++ test.cpp -O2; ./a.out Time scalar: 3540 ms Time INT: 990 ms, speed up 3.57576 Time SIMD: 880 ms, speed up 4.02273 Results are the same.When I change
for ( int i = 0; i < N; i+=1 ) sum = sum ^ data[i];to equivalent of:
for ( int i = 0; i < N; i+=8 ) sum = (data[i] ^ data[i+1]) ^ (data[i+2] ^ data[i+3]) ^ (data[i+4] ^ data[i+5]) ^ (data[i+6] ^ data[i+7]) ^ sum;i do see improvment in scalar speed by factor of 2. But I don't see improvements in speed up. Before: intSpeedUp 3.98416, SIMDSpeedUP 12.5283. After: intSpeedUp 3.5572, SIMDSpeedUP 6.8523.
-vec-report3标志并查看循环是否真正向量化了？ - arunmoezhi_mm_load_si128？ - user3528438