Openmp和std :: vector上的还原? [英] Openmp and reduction on std::vector?

问题描述

我想使这段代码平行:

std::vector<float> res(n,0);
std::vector<float> vals(m);
std::vector<float> indexes(m);
// fill indexes with values in range [0,n)
// fill vals and indexes
for(size_t i=0; i<m; i++){
  res[indexes[i]] += //something using vas[i];
}

在此文章中，建议使用:

#pragma omp parallel for reduction(+:myArray[:6])

在这个问题中，在评论部分提出了相同的方法.

In this question the same approach is proposed in the comments section.

我有两个问题:

1. 在编译时我不知道m，从这两个示例看来，这是必需的.是这样吗?或者，如果我可以在这种情况下使用它，那么我必须在以下命令#pragma omp parallel for reduction(+:res[:?])中将?替换为什么? m或n?
2. for的索引相对于indexes和vals而不是相对于res是否有意义，尤其是考虑到reduction是在后者上完成的?

1. I don't know m at compile time, and from these two examples it seems that's required. Is it so? Or if I can use it for this case, what do I have to replace ? with in the following command #pragma omp parallel for reduction(+:res[:?]) ? m or n?
2. Is it relevant that the indexes of the for are relative to indexes and vals and not to res, especially considering that reduction is done on the latter one?

但是，如果可以，我该如何解决这个问题?

However, If so, how can I solve this problem?

推荐答案

为用户声明的特定类型C ++向量的归约化是相当简单的:

It is fairly straight forward to do a user declared reduction for C++ vectors of a specific type:

#include <algorithm>
#include <vector>

#pragma omp declare reduction(vec_float_plus : std::vector<float> : \
                              std::transform(omp_out.begin(), omp_out.end(), omp_in.begin(), omp_out.begin(), std::plus<float>())) \
                    initializer(omp_priv = decltype(omp_orig)(omp_orig.size()))

std::vector<float> res(n,0);
#pragma omp parallel for reduction(vec_float_plus : res)
for(size_t i=0; i<m; i++){
    res[...] += ...;
}

1a)不需要在编译时不知道m.

1a) Not knowing m at compile time is not a requirement.

1b)您不能在std::vector上使用数组节缩减，因为它们不是数组(并且std::vector::data不是标识符).如果可能的话，您必须使用n，因为这是数组部分中的元素数.

1b) You cannot use the array section reduction on std::vectors, because they are not arrays (and std::vector::data is not an identifier). If it were possible, you'd have to use n, as this is the number of elements in the array section.

2)只要您只阅读indexes和vals，就没有问题.

2) As long as you are only reading indexes and vals, there is no issue.

原始的initializer原因更简单:initializer(omp_priv = omp_orig).但是，如果原始副本没有满零，那么结果将是错误的.因此，我建议使用更复杂的初始化程序，该初始化程序始终创建零元素向量.

The original initializer caluse was simpler: initializer(omp_priv = omp_orig). However, if the original copy is then not full of zeroes, the result will be wrong. Therefore, I suggest the more complicated initializer which always creates zero-element vectors.

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