c - 使用openmp优化MSE算法

Question

我想使用 openMP 优化以下代码

double val;
double m_y = 0.0f;
double m_u = 0.0f;
double m_v = 0.0f;

#define _MSE(m, t) \
val = refData[t] - calData[t];  \
m += val*val; 

#pragma omp parallel 
 {
 #pragma omp for
for( i=0; i<(width*height)/2; i++ ) {  //yuv422: 2 pixels at a time
    _MSE(m_u, 0); 
    _MSE(m_y, 1); 
    _MSE(m_v, 2); 
    _MSE(m_y, 3); 

  #pragma omp reduction(+:refData) reduction(+:calData)
    refData += 4;
    calData += 4;
 // int id = omp_get_thread_num();
 //printf("Thread %d performed %d iterations of the loop\n",id ,i);
}

}

目前我有错误的输出，欢迎任何优化上述代码的建议。

Answer 1

我认为您可以做的最简单的事情是让它分成 4 个线程，并计算每个线程中的 UYVY 错误。不要让它们成为单独的值，而是让它们成为一个数组:

double sqError[4] = {0};
const int numBytes = width * height * 2;

#pragma omp parallel for
for( int elem = 0; elem < 4; elem++ ) {
    for( int i = elem; i < numBytes; i += 4 ) {
        int val = refData[i] - calData[i];
        sqError[elem] += (double)(val*val);
    }
}

这样，每个线程都只对一件事进行操作，并且没有争用。

也许这不是 OMP 的最高级用途，但您应该会看到加速。

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在您评论性能下降后，我做了一些实验，发现性能确实更差。我怀疑这可能是由于缓存未命中造成的。

你说:

performance hit this time with openMP : Time :0.040637 with serial Time :0.018670

所以我使用每个变量的约简和单个循环对其进行了重新设计:

    #pragma omp parallel for reduction(+:e0) reduction(+:e1) reduction(+:e2) reduction(+:e3)
    for( int i = 0; i < numBytes; i += 4 ) {
        int val = refData[i] - calData[i];
        e0 += (double)(val*val);
        val = refData[i+1] - calData[i+1];
        e1 += (double)(val*val);
        val = refData[i+2] - calData[i+2];
        e2 += (double)(val*val);
        val = refData[i+3] - calData[i+3];
        e3 += (double)(val*val);
    }

在我的 4 核机器上测试用例，我观察到不到 4 倍的改进:

serial:             2025 ms
omp with 2 loops:   6850 ms
omp with reduction: 455  ms

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[编辑] 关于为什么第一段代码的性能比非并行版本差的问题，Hristo Iliev 说:

Your first piece of code is a terrible example of what false sharing does in multithreaded codes. As sqError has only 4 elements of 8 bytes each, it fits in a single cache line (even in a half cache line on modern x86 CPUs). With 4 threads constantly writing to neighbouring elements, this would generate a massive amount of inter-core cache invalidation due to false sharing. One can get around this by using instead a structure like this struct _error { double val; double pad[7]; } sqError[4]; Now each sqError[i].val will be in a separate cache line, hence no false sharing.