Performance assigning and copying with StaticArrays.jl in Julia

user_12365807 0
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I was thinking of using the package StaticArrays.jl to enhance the performance of my code. However, I only use arrays to store computed variables and use them later after certain conditions are set. Hence, I was benchmarking the type SizedVector in comparison with normal vector, but I do not understand to code below. I also tried StaticVector and used the work around Setfield.jl.

using StaticArrays, BenchmarkTools, Setfield
function copySized(n::Int64)
    v = SizedVector{n, Int64}(zeros(n))
    w = Vector{Int64}(undef, n)
    for i in eachindex(v)
        v[i] = i
    end
    for i in eachindex(v)
        w[i] = v[i]
    end
end
function copyStatic(n::Int64)
    v = @SVector zeros(n)
    w = Vector{Int64}(undef, n)
    for i in eachindex(v)
        @set v[i] = i
    end
    for i in eachindex(v)
        w[i] = v[i]
    end
end
function copynormal(n::Int64)
    v = zeros(n)
    w = Vector{Int64}(undef, n)
    for i in eachindex(v)
        v[i] = i
    end
    for i in eachindex(v)
        w[i] = v[i]
    end
end
n = 10
@btime copySized($n)
@btime copyStatic($n)
@btime copynormal($n)

3.950 μs (42 allocations: 2.08 KiB)
5.417 μs (98 allocations: 4.64 KiB) 
78.822 ns (2 allocations: 288 bytes)

Why does the case with SizedVector does have some much more allocations and hence worse performance? Do I not use SizedVector correctly? Should it not at least have the same performance as normal arrays?

Thank you in advance.

Cross post of Julia Discourse

julia memory allocation arrays performance
2 Answers

I feel this is apples-to oranges comparison (and size should be store in statically in type). More illustrative code could look like this:

function copySized(::Val{n}) where n
    v = SizedVector{n}(1:n)
    w = Vector{Int64}(undef, n)
    w .= v
end
function copyStatic(::Val{n}) where n
    v =  SVector{n}(1:n)
    w = Vector{Int64}(undef, n)
    w .= v
end
function copynormal(n) 
    v = [1:n;]
    w = Vector{Int64}(undef, n)
    w .= v
end

And now benchamrks:

julia> n = 10
10

julia> @btime copySized(Val{$n}());
  248.138 ns (1 allocation: 144 bytes)

julia> @btime copyStatic(Val{$n}());
  251.507 ns (1 allocation: 144 bytes)

julia> @btime copynormal($n);
  77.940 ns (2 allocations: 288 bytes)

julia>

julia>

julia> n = 1000
1000

julia> @btime copySized(Val{$n}());
  840.000 ns (2 allocations: 7.95 KiB)

julia> @btime copyStatic(Val{$n}());
  830.769 ns (2 allocations: 7.95 KiB)

julia> @btime copynormal($n);
  1.100 μs (2 allocations: 15.88 KiB)
user_99577100

@phipsgabler is right! Statically sized arrays have their performance advantages when the size is known statically, at compile time. My arrays are, however, dynamically sized, with the size n being a runtime variable.

Changing this yields more sensible results:

using StaticArrays, BenchmarkTools, Setfield
function copySized()
    v = SizedVector{10, Float64}(zeros(10))
    w = Vector{Float64}(undef, 10*2)
    for i in eachindex(v)
        v[i] = rand()
    end
    for i in eachindex(v)
        j = i+floor(Int64, 10/4)
        w[j] = v[i]
    end
end
function copyStatic()
    v = @SVector zeros(10)
    w = Vector{Int64}(undef, 10*2)
    for i in eachindex(v)
       @set v[i] = rand()
    end
    for i in eachindex(v)
        j = i+floor(Int64, 10/4)
        w[j] = v[i]
    end
end
function copynormal()
    v = zeros(10)
    w = Vector{Float64}(undef, 10*2)
    for i in eachindex(v)
        v[i] = rand()
    end
    for i in eachindex(v)
        j = i+floor(Int64, 10/4)
        w[j] = v[i]
    end
end
@btime copySized()
@btime copyStatic()
@btime copynormal()

110.162 ns (3 allocations: 512 bytes)
48.133 ns (1 allocation: 224 bytes)
92.045 ns (2 allocations: 368 bytes)
user_123658070
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