Julia 中 @distributed 宏下的混淆范围规则

Posted 2023-03-27

【中文标题】Julia 中 @distributed 宏下的混淆范围规则

【英文标题】：Confusing scoping rules under @distributed macro in julia

【发布时间】：2021-11-07 07:27:44

【问题描述】：

我正在尝试在 Julia 中并行化代码，但遇到了一个奇怪的范围问题。将局部变量传递给 @distributed for 循环中的函数时，我不理解范围规则。执行以下代码时，您会得到预期的行为

using Distributed
addprocs(4)

@everywhere function k(x)
    println("x = ", x)
    return x
end

sumk = 0
sumk += @distributed (+) for i in 2:nprocs()
        k(myid())
    end
println("sumk = ", sumk)

运行此代码给出

      From worker 4:    x = 4
      From worker 2:    x = 2
      From worker 3:    x = 3
      From worker 5:    x = 5
14

sumk = 14

现在，我稍微修改一下代码

using Distributed
addprocs(4)

@everywhere function k(x)
    println("x = ", x)
    return x
end

@everywhere x = myid()

sumk = 0
sumk += @distributed (+) for i in 2:nprocs()
        k(x)
    end
println("sumk = ", sumk)

在执行时会给出以下结果：

      From worker 2:    x = 1
      From worker 4:    x = 1
      From worker 5:    x = 1
      From worker 3:    x = 1
4

sumk = 4

在这里，我不明白为什么 myid() 在本地工作，但 x 仅取自进程 1。

感谢您的帮助。

【参考方案1】：

看起来@everywhere 宏的右侧是在本地计算的。

你可以这样做：

remote_do.( Ref(()->global x = myid()), workers())

现在：

@distributed (+) for i in 2:nprocs()
               k(x)
           end
      From worker 3:    x = 3
      From worker 2:    x = 2
      From worker 5:    x = 5
      From worker 4:    x = 4
14