OpenMP多个线程更新同一阵列 [英] OpenMP multiple threads update same array

问题描述

我的程序中包含以下代码，我想使用OpenMP对其进行加速.

I have the following code in my program and I want to accelerate it using OpenMP.

...
for(i=curr_index; i < curr_index + rx_size; i+=2){ 
    int64_t tgt = rcvq[i];
    int64_t src = rcvq[i+1];
    if (!TEST(tgt)) {
        pred[tgt] = src;
        newq[newq_count++] = tgt;
    }
} 

当前，我的版本如下:

...
chunk = rx_sz / omp_nthreads;

#pragma omp parallel for num_threads(omp_nthreads)
for (ii = 0; ii < omp_nthreads; ii++) { 
    int start = curr_index + ii * chunk;
    for (index = start; index < start + chunk; index +=2) { 
        int64_t tgt = rcvq[index];
        int64_t src = rcvq[index+1];
        if (!TEST(tgt)) {
            pred[tgt] = src;

            #pragma omp critical 
            newq[newq_count++] = tgt;
        }
    }
}

运行OpenMP版本时，与原始版本相比，性能会大大下降.我认为问题可能是由于紧急操作"而导致，它阻止了并行处理.我想知道我的代码可以增强什么，因此我可以获得比串行版本更好的性能.在代码中，rx_sz始终是omp_nthreads的倍数.

When I run the OpenMP version, I see a big performance degradation compared to the original version. I think the issue could be because of "omp critical" which prevents parallel processing. I want to know what could be enhanced with my code, so I could get better performance over the serial version. In the code, rx_sz is always a multiple of omp_nthreads.

推荐答案

我很确定omp关键部分目前会限制您的性能.

I'm pretty sure omp critical section limiting your performance at this point.

我建议您将结果收集到单独的缓冲区/向量中，并在并行处理完成后合并它们(当然，如果顺序对您而言无关紧要)

I'd recommend you to collect the results into separate buffers/vectors and merge them after the parallel processing is done (of course, if the order doesn't matter for you)

vector<vector<int64_t>> res;
res.resize(num_threads);
#pragma omp parallel for
for (index = 0; index < rx_sz/2; ++index) { 
        int64_t tgt = rcvq[2*index];
        int64_t src = rcvq[2*index+1];
        if (!TEST(tgt)) {
            pred[tgt] = src;

            res[omp_get_thread_num()].push_back(tgt);
        }
    }

// Merge all res vectors if needed

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