我想让这段代码并行:
std::vectorres(n,0);std::vector值(米);std::vector指数(米);//用 [0,n) 范围内的值填充索引//填充值和索引for(size_t i=0; i
在这篇文章中,建议使用:>
#pragma omp parallel for reduction(+:myArray[:6])
在这个问题中,评论部分提出了相同的方法.>
我有两个问题:
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,我必须用以下命令替换 ?
用以下命令 #pragma omp parallel for reduction(+:res[:?])
?m
还是 n
?for
的索引是否与 indexes
和 vals
相关,而不与 res
相关, 特别是考虑到 reduction
是在后一个上做的?但是,如果是这样,我该如何解决这个问题?
对特定类型的 C++ 向量执行用户声明的归约是相当直接的:
#include <算法>#include <向量>#pragma omp 声明减少(vec_float_plus : std::vector<float> : std::transform(omp_out.begin(), omp_out.end(), omp_in.begin(), omp_out.begin(), std::plus())) 初始值设定项(omp_priv = decltype(omp_orig)(omp_orig.size()))std::vectorres(n,0);#pragma omp 并行减少(vec_float_plus : res)for(size_t i=0; i
1a) 在编译时不知道 m
不是必需的.
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
不是一个标识符).如果可能,您必须使用 n
,因为这是数组部分中的元素数.
2) 只要您只读取indexes
和vals
,就没有问题.
原来的initializer
caluse 更简单:initializer(omp_priv = omp_orig)
.但是,如果原始副本没有全零,结果将是错误的.因此,我建议使用更复杂的初始化器,它总是创建零元素向量.
I want to make this code parallel:
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];
}
In this article it's suggested to use:
#pragma omp parallel for reduction(+:myArray[:6])
In this question the same approach is proposed in the comments section.
I have two questions:
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
?for
are relative to indexes
and vals
and not to res
, especially considering that reduction
is done on the latter one?However, If so, how can I solve this problem?
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) Not knowing m
at compile time is not a requirement.
1b) You cannot use the array section reduction on std::vector
s, 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) As long as you are only reading indexes
and vals
, there is no issue.
Edit: 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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