我试图在Julia中使用并行性来构造几个大型类型(特别是通过sklearn通过PyCall的高斯混合模型).
如果我是这样做的,我会这样做:
models = Array(GMM, N)
for i = 1 : N
params = ...
models[i] = train_gmm(params)
end
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但是,我应该能够做到这一点并行.我无法确定从哪里开始,因为SharedArrays和@parallel对我来说似乎不对.
我试图使用@spawn,但发现了以下内容:
function f1()
rand(10000000)
rand(10000000)
rand(10000000)
rand(10000000)
rand(10000000)
end
function f2()
a = @spawn rand(10000000)
b = @spawn rand(10000000)
c = @spawn rand(10000000)
d = @spawn rand(10000000)
e = @spawn rand(10000000)
a_r = fetch(a)
b_r = fetch(b)
c_r = fetch(c)
d_r = fetch(d)
e_r = fetch(e)
end
f1()
f2()
println(@elapsed(f1()))
println(@elapsed(f2()))
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f1需要0.21秒,f2需要0.32秒!@spawn有什么东西我错过了吗?
编辑
它看起来像:
function f1()
[sum(rand(100000000)),
sum(rand(100000000)),
sum(rand(100000000)),
sum(rand(100000000)),
sum(rand(100000000))]
end
function f2()
a = @spawn sum(rand(100000000))
b = @spawn sum(rand(100000000))
c = @spawn sum(rand(100000000))
d = @spawn sum(rand(100000000))
e = @spawn sum(rand(100000000))
[fetch(a), fetch(b), fetch(c), fetch(d), fetch(e)]
end
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导致f2()比f1()运行得更快,并且它更符合我的要求.除非有人有更好的官方方式,否则我会继续这样做.
谢谢.
我认为你的编辑是正确的.
IAINMAC:~ idunning$ julia -p 3
julia> @everywhere function foo()
sleep(2)
end
julia> @time [foo(), foo(), foo()]
elapsed time: 6.017959282 seconds (294088 bytes allocated)
3-element Array{Nothing,1}:
nothing
nothing
nothing
julia> function bar()
a = @spawn foo()
b = @spawn foo()
c = @spawn foo()
[fetch(a), fetch(b), fetch(c)]
end
bar (generic function with 1 method)
julia> @time bar()
elapsed time: 2.030760103 seconds (199720 bytes allocated)
3-element Array{Nothing,1}:
nothing
nothing
nothing
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甚至更优雅pmap
:
julia> @everywhere function foo(a::Int)
sleep(a)
end
julia> @time pmap(foo,1:3)
elapsed time: 3.004821524 seconds (448540 bytes allocated)
3-element Array{Any,1}:
nothing
nothing
nothing
julia> @time map(foo,1:3)
elapsed time: 6.006557822 seconds (1368 bytes allocated)
3-element Array{Nothing,1}:
nothing
nothing
nothing
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