如何与openMP平行处理浮点以正确

Question

经过数小时的调试和小规模的例子，试图找到自己做错了什么，我终于得到了我的理论之一。 浮点四舍五入错误。 浮点计算是非相关的计算机

什么可以这样做以减少错误吗？

代码：

                        double energy = 0;
                        double contrast = 0;
                        double homogeneity = 0;
                        double entropy = 0;
                        double correlation = 0;
                        double shade = 0;
                        double prominence = 0;
                        double glcmMean = 0;
                        double sigma = 0;
                        double squaredVarianceIntensity = 0;
                        double A = 0;
                        double B = 0;
                        
                        for(int c = 0; c <normalizedGlcm.cols; c++){
                            #pragma omp parallel for reduction(+:homogeneity,energy,contrast,entropy,glcmMean)
                            for(int r = 0; r<normalizedGlcm.rows; r++){

                                double pij = normalizedGlcm.at<double>(r,c,0);
                                double intensity = (double)img.at<uchar>(col,row,0);

                                if(pij != 0){
                                    homogeneity += pij/(1.0+((c-r)*(c-r)));
                                    energy += pij * pij;
                                    contrast += (c-r)*(c-r)*pij;
                                    entropy += -(log(pij)*pij); // pij will never be under 0
                                    glcmMean += pij * intensity;
                                }
                            }
                        }

之后，Glcmmean变量有更多的循环和其他一些计算。到目前为止，我只有Glcmmean变量才会出现错误。 错误示例：

serial     -      parrallel
1.66905e+28 vs 1.55964e+30
4.09033e+28 vs 3.62704e+30
8.38877e+30 vs 3.35551e+31

Answer 1

基于注释中的信息，您只需尝试以下累积glcmmean的代码，这样累积的值比初始代码中的数量级更多。此假设normalizedglcm.cols和normolizedglcm.rows相对较近（例如，不是2和2000）。

double energy = 0;
double contrast = 0;
double homogeneity = 0;
double entropy = 0;
double correlation = 0;
double shade = 0;
double prominence = 0;
double glcmMean = 0;
double sigma = 0;
double squaredVarianceIntensity = 0;
double A = 0;
double B = 0;

for(int c = 0; c <normalizedGlcm.cols; c++){
    double local_homogeneity = 0;
    double local_energy = 0;
    double local_contrast = 0;
    double local_entropy = 0;
    double local_glcmMean = 0;

    #pragma omp parallel for reduction(+:local_homogeneity,local_energy,local_contrast,local_entropy,local_glcmMean)
    for(int r = 0; r<normalizedGlcm.rows; r++){
        double pij = normalizedGlcm.at<double>(r,c,0);
        double intensity = (double)img.at<uchar>(col,row,0);

        if(pij != 0){
            local_homogeneity += pij/(1.0+((c-r)*(c-r)));
            local_energy += pij * pij;
            local_contrast += (c-r)*(c-r)*pij;
            local_entropy += -(log(pij)*pij); // pij will never be under 0
            local_glcmLocalMean += pij * intensity;
        }
    }

    homogeneity += local_homogeneity;
    energy += local_energy;
    contrast += local_contrast;
    entropy += local_entropy;
    glcmMean += local_glcmMean;
}

如果问题是由于FP饱和度 /不精确，则应强烈提高结果的准确性，尤其是在顺序中。