更新时间:2022-10-26 09:33:18
c $ c> O3 标志会自动打开-ftree-vectorize。 https://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html
-O3打开-O2指定的所有优化,并打开-finline函数,-funswitch-loops ,-fpredictive-commoning,-fgc-after-reload,-ftree-loop-vectorize,-ftree-loop-distributions-patterns,-ftree-slp-vectorize,-fvect-cost-model,-ftree-partial-pre和-fipa-cp-clone选项
因此在这两种情况下,编译器都试图进行循环向量化。
使用g ++ 4.8.2编译:
g ++ test.cpp -O2 -std = c ++ 0x -funroll-loops -ftree-vectorize -ftree-vectorizer-verbose = 1 -o test
给出:
在test.cpp分析循环:16
矢量化循环在test.cpp:16
test.cpp:16:注意:创建运行时检查数据引用* it2 $ _M_current_106和* _39
test.cpp:16:note:created 1版本控制别名检查。
test.cpp:16:note:LOOP VECTORIZED。
在test_old.cpp分析循环:29
test.cpp:22:note:vectorized 1个循环的函数。
test.cpp:18:note:展开循环7次
test.cpp:16:note:展开循环7次
test。 cpp:28:note:展开循环1次
编译时不带 -vectorize
标志:
g ++ test.cpp -O2 -std = c ++ 0x -funroll -loops -ftree-vectorizer-verbose = 1 -o test
仅返回:
test_old.cpp:16:注意:展开循环7次
test_old.cpp:28:note:Unroll loop 1次
第16行是循环函数的开始,因此编译器肯定是矢量化。检查汇编器也确认这一点。
我目前正在使用的笔记本电脑上似乎正在获取一些积极的缓存,这使得很难准确测量函数需要运行多长时间。
使用 __ restrict __
限定词告诉编译器数组之间没有重叠。
编译器数组与 __ builtin_assume_aligned
(不可移植)对齐。
这是我生成的代码(我删除了模板,因为您将要为不同的数据类型使用不同的对齐方式)
#include< ; iostream>
#include< chrono>
#include< vector>
void foo(double * __restrict__ p1,
double * __restrict__ p2,
size_t start,
size_t end)
{
double * pA1 = static_cast< double *>(__ builtin_assume_aligned(p1,16));
double * pA2 = static_cast< double *>(__ builtin_assume_aligned(p2,16));
for(size_t i = start; i {
pA1 [i] = pA1 [i] -pA2 [i]
pA1 [i] + = 1;
}
}
int main()
{
size_t n;
double x,y;
n = 12800000;
std :: vector< double> v,u;
for(size_t i = 0; i x = i;
y = i - 1;
v.push_back(x);
u.push_back(y);
}
使用命名空间std :: chrono;
high_resolution_clock :: time_point t1 = high_resolution_clock :: now();
foo(& v [0],& u [0],0,n);
high_resolution_clock :: time_point t2 = high_resolution_clock :: now();
duration< double> time_span = duration_cast< duration< double>>(t2-t1);
std :: cout<< 它带我< time_span.count()<< seconds。;
std :: cout<< std :: endl;
return 0;就像我说的,我有麻烦得到一致的时间测量,所以可以''
}
The introductory links I found while searching:
As you can see most of them are for C, but I thought that they might work at C++ as well. Here is my code:
template<typename T>
//__attribute__((optimize("unroll-loops")))
//__attribute__ ((pure))
void foo(std::vector<T> &p1, size_t start,
size_t end, const std::vector<T> &p2) {
typename std::vector<T>::const_iterator it2 = p2.begin();
//#pragma simd
//#pragma omp parallel for
//#pragma GCC ivdep Unroll Vector
for (size_t i = start; i < end; ++i, ++it2) {
p1[i] = p1[i] - *it2;
p1[i] += 1;
}
}
int main()
{
size_t n;
double x,y;
n = 12800000;
vector<double> v,u;
for(size_t i=0; i<n; ++i) {
x = i;
y = i - 1;
v.push_back(x);
u.push_back(y);
}
using namespace std::chrono;
high_resolution_clock::time_point t1 = high_resolution_clock::now();
foo(v,0,n,u);
high_resolution_clock::time_point t2 = high_resolution_clock::now();
duration<double> time_span = duration_cast<duration<double>>(t2 - t1);
std::cout << "It took me " << time_span.count() << " seconds.";
std::cout << std::endl;
return 0;
}
I used al the hints one can see commented above, but I did not get any speedup, as a sample output shows (with the first run having uncommented this #pragma GCC ivdep Unroll Vector
:
samaras@samaras-A15:~/Downloads$ g++ test.cpp -O3 -std=c++0x -funroll-loops -ftree-vectorize -o test
samaras@samaras-A15:~/Downloads$ ./test
It took me 0.026575 seconds.
samaras@samaras-A15:~/Downloads$ g++ test.cpp -O3 -std=c++0x -o test
samaras@samaras-A15:~/Downloads$ ./test
It took me 0.0252697 seconds.
Is there any hope? Or the optimization flag O3
just does the trick? Any suggestions to speedup this code (the foo
function) are welcome!
My version of g++:
samaras@samaras-A15:~/Downloads$ g++ --version
g++ (Ubuntu 4.8.1-2ubuntu1~12.04) 4.8.1
Notice that the body of the loop is random. I am not interesting in re-writing it in some other form.
EDIT
An answer saying that there is nothing more that can be done is also acceptable!
The O3
flag turns on -ftree-vectorize automatically. https://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html
-O3 turns on all optimizations specified by -O2 and also turns on the -finline-functions, -funswitch-loops, -fpredictive-commoning, -fgcse-after-reload, -ftree-loop-vectorize, -ftree-loop-distribute-patterns, -ftree-slp-vectorize, -fvect-cost-model, -ftree-partial-pre and -fipa-cp-clone options
So in both cases the compiler is trying to do loop vectorization.
Using g++ 4.8.2 to compile with:
g++ test.cpp -O2 -std=c++0x -funroll-loops -ftree-vectorize -ftree-vectorizer-verbose=1 -o test
Gives this:
Analyzing loop at test.cpp:16
Vectorizing loop at test.cpp:16
test.cpp:16: note: create runtime check for data references *it2$_M_current_106 and *_39
test.cpp:16: note: created 1 versioning for alias checks.
test.cpp:16: note: LOOP VECTORIZED.
Analyzing loop at test_old.cpp:29
test.cpp:22: note: vectorized 1 loops in function.
test.cpp:18: note: Unroll loop 7 times
test.cpp:16: note: Unroll loop 7 times
test.cpp:28: note: Unroll loop 1 times
Compiling without the -ftree-vectorize
flag:
g++ test.cpp -O2 -std=c++0x -funroll-loops -ftree-vectorizer-verbose=1 -o test
Returns only this:
test_old.cpp:16: note: Unroll loop 7 times
test_old.cpp:28: note: Unroll loop 1 times
Line 16 is the start of the loop function, so the compiler is definitely vectorizing it. Checking the assembler confirms this too.
I seem to be getting some aggressive caching on the laptop I'm currently using which is making it very hard to accurately measure how long the function takes to run.
But here's a couple of other things you can try too:
Use the __restrict__
qualifier to tell the compiler that there is no overlap between the arrays.
Tell the compiler the arrays are aligned with __builtin_assume_aligned
(not portable)
Here's my resulting code (I removed the template since you will want to use different alignment for different data types)
#include <iostream>
#include <chrono>
#include <vector>
void foo( double * __restrict__ p1,
double * __restrict__ p2,
size_t start,
size_t end )
{
double* pA1 = static_cast<double*>(__builtin_assume_aligned(p1, 16));
double* pA2 = static_cast<double*>(__builtin_assume_aligned(p2, 16));
for (size_t i = start; i < end; ++i)
{
pA1[i] = pA1[i] - pA2[i];
pA1[i] += 1;
}
}
int main()
{
size_t n;
double x, y;
n = 12800000;
std::vector<double> v,u;
for(size_t i=0; i<n; ++i) {
x = i;
y = i - 1;
v.push_back(x);
u.push_back(y);
}
using namespace std::chrono;
high_resolution_clock::time_point t1 = high_resolution_clock::now();
foo(&v[0], &u[0], 0, n );
high_resolution_clock::time_point t2 = high_resolution_clock::now();
duration<double> time_span = duration_cast<duration<double>>(t2 - t1);
std::cout << "It took me " << time_span.count() << " seconds.";
std::cout << std::endl;
return 0;
}
Like I said I've had trouble getting consistent time measurements, so can't confirm if this will give you a performance increase (or maybe even decrease!)