Construction of big MArrays from arrays is slow

[BeastyBlacksmith]

Quick timing:

@time MVector{5_000}(rand(5000))
745.251386 seconds (6.46 M allocations: 310.818 MiB, 0.02% gc time)

That is really hurting. From the README and the docs I suspected using MArrays is fine for big arrays unlike SArrays.

[ChrisRackauckas]

This shouldn't be expected to be fast. All of the specializations that are being done are specifically based on the number of elements that will not scale well.

From the README and the docs I suspected using MArrays is fine for big arrays unlike SArrays.

What text in the README led to this? That part should be clarified.

[BeastyBlacksmith]

Well essentially it is that the README is all about SArrays and does not mention the other types. So maybe a sentence like "Any type of the StaticArrays package is performant for small arrays ( ~ 100 elements ) only" would do, if it is correct.

[andyferris]

Yes, sorry if that wasn't clear, the StaticArrays methods aren't really well suited for large arrays. Julia isn't really built for efficiently handling (i.e. compiling code for) tuples of 5000 elements (and internally StaticArrays works with tuples).

[BeastyBlacksmith]

By revisiting the docs, for my case a SizedArray will be most suitable (construction is still 3x slower compared to plain Array, but thats fine), so that sentence from above is a little too harsh.

[timholy]

@BeastyBlacksmith, "performance" isn't quite the right word here: most/all of what you're measuring is probably compile time, not run time. Suppose you have a function foo that takes an AbstractVector. If you call foo on a regular Vector, Julia will compile just one version of foo and it will work for vectors of length n and n+1. With SArray/MArray, the compiler will generate separate versions of foo specialized for n and n+1. That makes a lot of sense for small vectors (you can get rid of loops, for example, and increase the performance of an algorithm that has to run in 1, 2, or 3 dimensions), but there is typically little point for large ones.

Compilation is slow, especially for large tuples. You might still find that, once compiled, the methods run quite quickly. Which is why "performance" is not a very good way of describing the problem here.

[chethega]

You might still find that, once compiled, the methods run quite quickly.

This is not the problem here:

julia> using StaticArrays
julia> N=500;r=rand(N); 
julia> @time MVector{N}(r);
  1.555657 seconds (1.07 M allocations: 54.239 MiB, 14.38% gc time)
julia> @time MVector{N}(r);
  0.000044 seconds (17 allocations: 8.922 KiB)

We see that the amount of allocated space is too large compared to the expected 4 kb, so something is wrong. After compilation, this should be a simple memcopy + boundschecks.

julia> N=500;r=rand(N);
julia> @btime MVector{$N}($r);
  20.392 μs (13 allocations: 8.77 KiB)
julia> N=1000;r=rand(N);
julia> @btime MVector{$N}($r);
  36.625 μs (14 allocations: 16.59 KiB)
julia> @btime copy($r);
  974.400 ns (1 allocation: 7.94 KiB)

[c42f]

@chethega interpolating the $N doesn't appear to be working quite as I expected in btime here. The following seems confusing (julia-1.2-rc1):

julia> N=1000;r=rand(N);

julia> @btime MVector{$N}($r);
  3.978 μs (14 allocations: 16.59 KiB)

julia> @btime MVector{1000}($r);
  692.565 ns (1 allocation: 7.88 KiB)

[kaji331]

Finally, I found this issue and noticed the speed description in doc. In my routine work, when I wanna deal performance issues, it means many 'large' arrays T_T

[andyferris]

How large, if I may ask?

[kaji331]

About 30000 rows, 3000 to 1 million columns 获取Outlook for Android<https://aka.ms/AAb9ysg>

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