c – OpenMP令人尴尬地并行循环,没有加速

这应该显然非常简单,并且应该几乎线性加速.我在这里错过了什么？

完整代码：

#include <omp.h>
#include <vector>
#include <iostream>
#include <ctime>

void tic(),toc();

int main(int argc,const char *argv[])
{
    const int COUNT = 100;
    const size_t sz = 250000 * 200;
    std::vector<int> vec(sz,1);

    std::cout << "max threads: " << omp_get_max_threads()<< std::endl;

    std::cout << "serial reduction" << std::endl;
    tic();
    for(int c = 0; c < COUNT; ++ c) {
        double sum = 0;
        for(size_t i = 0; i < sz; ++ i)
            sum += vec[i];
    }
    toc();

    int *const ptr = vec.data();
    const int sz_i = int(sz); // some OpenMP implementations only allow parallel for with int

    std::cout << "parallel reduction" << std::endl;
    tic();
    for(int c = 0; c < COUNT; ++ c) {
        double sum = 0;
        #pragma omp parallel for default(none) reduction(+:sum)
        for(int i = 0; i < sz_i; ++ i)
            sum += ptr[i];
    }
    toc();

    std::cout << "serial memset" << std::endl;
    tic();
    for(int c = 0; c < COUNT; ++ c) {
        for(size_t i = 0; i < sz; ++ i)
            vec[i] = 0;
    }
    toc();

    std::cout << "parallel memset" << std::endl;
    tic();
    for(int c = 0; c < COUNT; ++ c) {
        #pragma omp parallel for default(none)
        for(int i = 0; i < sz_i; ++ i)
            ptr[i] = 0;
    }
    toc();

    return 0;
}

static clock_t ClockCounter;

void tic()
{
    ClockCounter = std::clock();
}

void toc()
{
    ClockCounter = std::clock() - ClockCounter;
    std::cout << "telapsed clock ticks: " << ClockCounter << std::endl;
}

运行此产生：

g++ omp_test.cpp -o omp_test --ansi -pedantic -fopenmp -O1
./omp_test
max threads: 12
serial reduction
  elapsed clock ticks: 1790000
parallel reduction
  elapsed clock ticks: 19690000
serial memset
  elapsed clock ticks: 3860000
parallel memset
  elapsed clock ticks: 20800000

如果我使用-O2运行,g可以优化串行减少,我得到零时间,因此-O1.另外,放omp_set_num_threads(1);使时间更相似,尽管仍有一些差异：

g++ omp_test.cpp -o omp_test --ansi -pedantic -fopenmp -O1
./omp_test
max threads: 1
serial reduction
  elapsed clock ticks: 1770000
parallel reduction
  elapsed clock ticks: 7370000
serial memset
  elapsed clock ticks: 2290000
parallel memset
  elapsed clock ticks: 3550000

这应该是相当明显的,我觉得我没有看到一些非常基本的东西.我的CPU是具有超线程的英特尔(R)Xeon(R)CPU E5-2640 0 @ 2.50GHz,但在具有4个内核且没有超线程的同事的i5中观察到相同的行为.我们都在运行Linux.

编辑

似乎一个错误是在时间方面,运行：

static double ClockCounter;

void tic()
{
    ClockCounter = omp_get_wtime();//std::clock();
}

void toc()
{
    ClockCounter = omp_get_wtime()/*std::clock()*/ - ClockCounter;
    std::cout << "telapsed clock ticks: " << ClockCounter << std::endl;
}

产生更“合理”的时间：

g++ omp_test.cpp -o omp_test --ansi -pedantic -fopenmp -O1
./omp_test
max threads: 12
serial reduction
  elapsed clock ticks: 1.80974
parallel reduction
  elapsed clock ticks: 2.07367
serial memset
  elapsed clock ticks: 2.37713
parallel memset
  elapsed clock ticks: 2.23609

但是,仍然没有加速,它只是不再慢.

EDIT2：

正如user8046所建议的那样,代码严重受内存限制.并且正如Z boson所建议的那样,串行代码很容易被优化掉,并且不确定这里测量的是什么.所以我做了一个小的改变,把总和放在循环之外,这样它在c的每次迭代都不会为零.我还用sum = F(vec [i])和memset操作替换了还原操作,其中vec [i] = F(i).运行方式：

g++ omp_test.cpp -o omp_test --ansi -pedantic -fopenmp -O1 -D"F(x)=sqrt(double(x))"
./omp_test
max threads: 12
serial reduction
  elapsed clock ticks: 23.9106
parallel reduction
  elapsed clock ticks: 3.35519
serial memset
  elapsed clock ticks: 43.7344
parallel memset
  elapsed clock ticks: 6.50351

计算平方根为线程增加了更多工作,最终有一些合理的加速(大约7倍,这是有意义的,因为超线程内核共享内存通道).