parallel-processing - Julia Parallel 宏似乎不起作用

Question

我第一次使用 julia 进行并行计算.我有点头疼。所以假设我开始 julia如下:julia -p 4 .然后我为所有处理器声明 a 函数，然后将它与 pmap 一起使用还有@parallel for .

@everywhere function count_heads(n)
    c::Int = 0
    for i=1:n
        c += rand(Bool)
    end
    n, c  # tuple (input, output)
end

###### first part ######
v=pmap(count_heads, 50000:1000:70000)
println("Result first part")
println(v)

###### second part ######
println("Result second part")
@parallel for i in 50000:1000:70000
    println(count_heads(i))
end

结果如下。

Result first part
Counting heads function
Any[(50000,24894),(51000,25559),(52000,26141),(53000,26546),(54000,27056),(55000,27426),(56000,28024),(57000,28380),(58000,29001),(59000,29398),(60000,30100),(61000,30608),(62000,31001),(63000,31520),(64000,32200),(65000,32357),(66000,33063),(67000,33674),(68000,34085),(69000,34627),(70000,34902)]
Result second part
    From worker 4:  (61000, From worker 5:  (66000, From worker 2:  (50000, From worker 3:  (56000

因此，函数 pmap显然工作正常，但 @parallel for正在停止，或者它没有给我结果。难道我做错了什么？

谢谢!

更新

如果在代码末尾我放了sleep(10) .它正确地完成了工作。

From worker 5:  (66000,33182)
From worker 3:  (56000,27955)
............
From worker 3:  (56000,27955)

Answer 1

您的两个示例都可以在我的笔记本电脑上正常运行，所以我不确定，但我认为这个答案可能会解决您的问题!

如果您添加 @sync，它应该可以正常工作在 @parallel for 之前

来自 julia 并行计算文档 http://docs.julialang.org/en/release-0.4/manual/parallel-computing/ :

... the reduction operator can be omitted if it is not needed. In that case, the loop executes asynchronously, i.e. it spawns independent tasks on all available workers and returns an array of RemoteRef immediately without waiting for completion. The caller can wait for the RemoteRef completions at a later point by calling fetch() on them, or wait for completion at the end of the loop by prefixing it with @sync, like @sync @parallel for.

所以你可能调用println在 RemoteRef 完成之前。