英文:

Regarding OpenMP Parallel SIMD Reductions

问题

我有一个相当简单的for循环，对一个非常大的double值数组 `x`（1亿个数据点）求和，使用C语言并行计算，使用指定数量的线程。根据我的阅读，OpenMP指令应该如下：

```c
int nthreads = 4, l = 1e8;
double sum = 0.0;

#pragma omp parallel for simd num_threads(nthreads) reduction(+:sum)
for (int i = 0; i < l; ++i) sum += x[i];

然而，这会导致编译器警告：

loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Wpass-failed=transform-warning]

而且，使用多个线程运行比单线程更慢。我正在使用Apple M1 Mac，并使用 clang (Xclang) v13.0.0 编译器。我想知道的是：这是我的系统问题，还是这个OpenMP指令确实存在问题/不可行？

<details>
<summary>英文:</summary>

I have a rather simple for loop summing a very large array of double values `x` (100 mio data points) in C. I want to do this in parallel with SIMD reductions, using a specified number of threads. The OpenMP instruction in my reading should be:

```c
int nthreads = 4, l = 1e8;
double sum = 0.0;

#pragma omp parallel for simd num_threads(nthreads) reduction(+:sum)
for (int i = 0; i &lt; l; ++i) sum += x[i];

This however gives a compiler warning

loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Wpass-failed=transform-warning]

and running it with multiple threads is slower than single threaded. I'm using the Apple M1 Mac with clang (Xclang) v13.0.0 compiler. What I would like to know is: is this an issue with my system or is there actually something wrong / infeasible with this OpenMP instruction?

答案1

得分: 0

这在 clang 版本大于等于 15 上编译时没有警告，但性能取决于系统。在 Apple M1 上，多线程似乎对 SIMD 向量化没有太多帮助，使用 #pragma omp simd reduction(+:sum) 指令的单线程执行效果差不多是最好的。

英文:

This compiles without warning on clang >= 15, but performance depends on the system. With the Apple M1 it seems that multithreading does not add much to the SIMD vectorization and single threaded execution with a #pragma omp simd reduction(+:sum) instruction is about as good as it gets.