修改ncnn的openmp异步处理方法 附C++样例代码

ncnn刚发布不久，博主在ios下尝试编译。

遇上了openmp的编译问题。

寻找各种解决方案无果，亲自操刀。

采用std::thread 替换 openmp。

ncnn项目地址:

https://github.com/Tencent/ncnn

后来询问ncnn的作者才知道在ios下的编译方法。

至此，当时的临时方案 采用std::thread 替换 openmp。

想想也许在一些特定情况下还是比较适用的，当前方便两者之间进行切换验证。

抽空写了一个示例项目。

项目地址:

https://github.com/cpuimage/ParallelFor

贴上完整代码:

#include <stdio.h>
#include <stdlib.h>
#include <iostream>

#if defined(_OPENMP)
// compile with: /openmp
#include <omp.h>
auto const epoch = omp_get_wtime();
double now() {
    return omp_get_wtime() - epoch;
};
#else
#include <chrono>
auto const epoch = std::chrono::steady_clock::now();
double now() {
    return std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - epoch).count() / 1000.0;
};
#endif

template<typename FN>
double bench(const FN &fn) {
    auto took = -now();
    return (fn(), took + now());
}

#include <functional>

#if defined(_OPENMP)
#    include <omp.h>
#else
#include <thread>

#include <vector>
#endif

#ifdef _OPENMP
static int processorCount = static_cast<int>(omp_get_num_procs());
#else
static int processorCount = static_cast<int>(std::thread::hardware_concurrency());
#endif

static void ParallelFor(int inclusiveFrom, int exclusiveTo, std::function<void(size_t)> func)
{
#if defined(_OPENMP)
#pragma omp parallel for num_threads(processorCount)
    for (int i = inclusiveFrom; i < exclusiveTo; ++i)
    {
        func(i);
    }
    return;
#else
    if (inclusiveFrom >= exclusiveTo)
        return;

    ;
    )
    {
        thread_cnt = std::thread::hardware_concurrency();
    }
    size_t entry_per_thread = (exclusiveTo - inclusiveFrom) / thread_cnt;

    )
    {
        for (int i = inclusiveFrom; i < exclusiveTo; ++i)
        {
            func(i);
        }
        return;
    }
    std::vector<std::thread> threads;
    int start_idx, end_idx;

    for (start_idx = inclusiveFrom; start_idx < exclusiveTo; start_idx += entry_per_thread)
    {
        end_idx = start_idx + entry_per_thread;
        if (end_idx > exclusiveTo)
            end_idx = exclusiveTo;

        threads.emplace_back([&](size_t from, size_t to)
        {
            for (size_t entry_idx = from; entry_idx < to; ++entry_idx)
                func(entry_idx);
        }, start_idx, end_idx);
    }

    for (auto& t : threads)
    {
        t.join();
    }
#endif
}

void test_scale(int i, double* a, double* b) {
    a[i] =  * b[i];
}

int main()
{
    ;
    double* a2 = (double*)calloc(N, sizeof(double));
    double* a1 = (double*)calloc(N, sizeof(double));
    double* b = (double*)calloc(N, sizeof(double));
    if (a1 == NULL || a2 == NULL || b == NULL)
    {
        if (a1)
        {
            free(a1);
        }if (a2)
        {
            free(a2);
        }if (b)
        {
            free(b);
        }
        ;
    }
    ; i < N; i++)
    {
        a1[i] = i;
        a2[i] = i;
        b[i] = i;
    }
    double beforeTime = bench([&] {
        ; i < N; i++)
        {
            test_scale(i, a1, b);
        }
    });

    std::cout << ) << "ms" << std::endl;
    double afterTime = bench([&] {
        ParallelFor(, N, [a2, b](size_t i)
        {
            test_scale(i, a2, b);
        });
    });
    std::cout << ) << "ms" << std::endl;

    ; i < N; i++)
    {
        if (a1[i] != a2[i]) {
            printf("error %f : %f \t", a1[i], a2[i]);
            getchar();
        }
    }
    free(a1);
    free(a2);
    free(b);
    getchar();
    ;
}

要使用OPENMP,加个编译选项/openmp  或者定义一下 _OPENMP 即可。

建议c++11编译。

示例代码比较简单。

ncnn代码修改例子如下:

   #pragma omp parallel for
        ; q<channels; q++)
        {
            const Mat m = src.channel(q);
            Mat borderm = dst.channel(q);

            copy_make_border_image(m, borderm, top, left, type, v);
        }

　改为

    ParallelFor(, channels, [&](int  q) {
                {
                    const Mat m = src.channel(q);
                    Mat borderm = dst.channel(q);

                    copy_make_border_image(m, borderm, top, left, type, v);
                }});

本来计划抽点时间把ncnn整体都改一下，发个修改版本出来。

想想还是把做法贴出来，给有需求的人吧。

自己动手丰衣足食。

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