问题描述
我试图用最优化的并行处理，一个难题获得更好的性能。
I'm trying to optimise a puzzle with parallel processing, for better performance.
在理想情况下，在C99瓦特/ OpenMP的，我应该能够做到这一点与的帮助的#pragma OMP并行的之前的所讨论循环，那么它应该是到系统散布在CPU之间的负载。
Ideally, in C99 w/ OpenMP, I should be able to do that with the help of a #pragma omp parallel for prior to a for loop in question, and then it should be up to the system to distribute the load between the CPUs.
有关围棋在时，然而，似乎表明，用于并行处理的我必须（0），手动从运行时环境获取内核的数量，（1）中，环在所述芯，（2），从而有效地code最大鲜明的for循环每个内核，（3），循环再次核心上，以确保所有的东西得到了处理。
The official documentation for Go at https://golang.org/doc/effective_go.html#parallel, however, seems to suggest that for parallel processing I must, (0), manually get the number of cores from the runtime environment, (1), loop over said cores, (2), effectively code up a distinct for loop for each core, (3), loop over the cores once again to make sure all the stuff got processed.
我缺少的东西吗？对于简单的情况下，是OpenMP的同古Ç优于全新的往那边走，被吹捧为C的最佳替代品？对于更复杂的例子，究竟你在CPU之间划分了范围？
Am I missing something? For the simplest case, is OpenMP with the ancient C superior to the brand new Go that's touted as C's best replacement? For a more complicated example, how exactly do you split up a range between the CPUs?
推荐答案
有效围棋是过时的这件事，去自动设置GOMAXPROCS到处理器的数量自动（您仍可以手动设置它迫使你想要的号码）。
Effective Go is outdated about that, Go automatically sets GOMAXPROCS to the number of processors automatically (you can still manually set it to force the number you want).
有关这里的一个切片的并行处理一个很简单的例子：
For here's a very simple example for parallel processing of a slice:
data := make([]float64, SZ)
var wg sync.WaitGroup
for i := range data {
wg.Add(1)
go func(v *float64) {
// note that using rand is a bad example because global rand uses a mutex
*v = rand.Float64()
wg.Done()
}(&data[i])
}
wg.Wait()
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