在Julia中为数组建立索引时避免内存分配 [英] Avoid memory allocation when indexing an array in Julia

问题描述

问题::我想在不触发内存分配的情况下对数组建立索引，尤其是在将索引元素传递到函数中时.通过阅读Julia文档，我怀疑答案围绕着sub函数，但是还不太清楚...

Question: I would like to index into an array without triggering memory allocation, especially when passing the indexed elements into a function. From reading the Julia docs, I suspect the answer revolves around using the sub function, but can't quite see how...

工作示例::我建立了Float64(x)的大向量，然后为x中的每个观察值建立了索引.

Working Example: I build a large vector of Float64 (x) and then an index to every observation in x.

N = 10000000
x = randn(N)
inds = [1:N]

现在，我将mean函数的时间设置在x和x[inds]上(我先运行mean(randn(2))以避免时间上的任何编译器异常):

Now I time the mean function over x and x[inds] (I run mean(randn(2)) first to avoid any compiler irregularities in the timing):

@time mean(x)
@time mean(x[inds])

这是一个相同的计算，但是正如预期的那样，计时结果是:

It's an identical calculation, but as expected the results of the timings are:

elapsed time: 0.007029772 seconds (96 bytes allocated)
elapsed time: 0.067880112 seconds (80000208 bytes allocated, 35.38% gc time)

那么，对于inds的任意选择(以及数组和函数的任意选择)，是否有办法解决内存分配问题?

So, is there a way around the memory allocation problem for arbitrary choices of inds (and arbitrary choice of array and function)?

推荐答案

也阅读tholy的答案以获得完整图片！

Read tholy's answer too to get a full picture!

使用索引数组时，Julia 0.4-pre(2015年2月开始)的情况现在并不好:

When using an array of indices, the situation is not great right now on Julia 0.4-pre (start of Feb 2015):

julia> N = 10000000;
julia> x = randn(N);
julia> inds = [1:N];
julia> @time mean(x)
elapsed time: 0.010702729 seconds (96 bytes allocated)
elapsed time: 0.012167155 seconds (96 bytes allocated)
julia> @time mean(x[inds])
elapsed time: 0.088312275 seconds (76 MB allocated, 17.87% gc time in 1 pauses with 0 full sweep)
elapsed time: 0.073672734 seconds (76 MB allocated, 3.27% gc time in 1 pauses with 0 full sweep)
elapsed time: 0.071646757 seconds (76 MB allocated, 1.08% gc time in 1 pauses with 0 full sweep)
julia> xs = sub(x,inds);  # Only works on 0.4
julia> @time mean(xs)
elapsed time: 0.057446177 seconds (96 bytes allocated)
elapsed time: 0.096983673 seconds (96 bytes allocated)
elapsed time: 0.096711312 seconds (96 bytes allocated)
julia> using ArrayViews
julia> xv = view(x, 1:N)  # Note use of a range, not [1:N]!
julia> @time mean(xv)
elapsed time: 0.012919509 seconds (96 bytes allocated)
elapsed time: 0.013010655 seconds (96 bytes allocated)
elapsed time: 0.01288134 seconds (96 bytes allocated)
julia> xs = sub(x,1:N)  # Works on 0.3 and 0.4
julia> @time mean(xs)
elapsed time: 0.014191482 seconds (96 bytes allocated)
elapsed time: 0.014023089 seconds (96 bytes allocated)
elapsed time: 0.01257188 seconds (96 bytes allocated)

• 因此，尽管我们可以避免分配内存，但实际上速度仍然较慢(！).
• 问题是通过数组而不是范围建立索引.您不能在0.3上使用sub，但可以在0.4上使用.
• 如果我们可以按范围进行索引，则可以在0.3上使用 ArrayViews.jl 其内置于0.4.这种情况与原始mean差不多.
• So while we can avoid the memory allocation, we are actually slower(!) still.
• The issue is indexing by an array, as opposed to a range. You can't use sub for this on 0.3, but you can on 0.4.
• If we can index by a range, then we can use ArrayViews.jl on 0.3 and its inbuilt on 0.4. This case is pretty much as good as the original mean.

我注意到使用较少数量的索引(而不是整个范围)时，间隙要小得多，并且内存分配很低，因此sub可能值得:

I noticed that with a smaller number of indices used (instead of the whole range), the gap is much smaller, and the memory allocation is low, so sub might be worth:

N = 100000000
x = randn(N)
inds = [1:div(N,10)]

@time mean(x)
@time mean(x)
@time mean(x)
@time mean(x[inds])
@time mean(x[inds])
@time mean(x[inds])
xi = sub(x,inds)
@time mean(xi)
@time mean(xi)
@time mean(xi)

给予

elapsed time: 0.092831612 seconds (985 kB allocated)
elapsed time: 0.067694917 seconds (96 bytes allocated)
elapsed time: 0.066209038 seconds (96 bytes allocated)
elapsed time: 0.066816927 seconds (76 MB allocated, 20.62% gc time in 1 pauses with 1 full sweep)
elapsed time: 0.057211528 seconds (76 MB allocated, 19.57% gc time in 1 pauses with 0 full sweep)
elapsed time: 0.046782848 seconds (76 MB allocated, 1.81% gc time in 1 pauses with 0 full sweep)
elapsed time: 0.186084807 seconds (4 MB allocated)
elapsed time: 0.057476269 seconds (96 bytes allocated)
elapsed time: 0.05733602 seconds (96 bytes allocated)

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