Benchmarks for unique and union methods

benchmacro.jl

macro stats(setupexpr,testexpr)
    quote
        N = 2^8
        results = zeros(N,3)
        for i = 1:N
            $setupexpr
            data = @timed $testexpr
            results[i,1] = data[2]
            results[i,2] = Float64(data[3])
            results[i,3] = data[4]
        end
        μ,σ = mean(results,1),std(results,1)
        println("time:       $(μ[1]) ± $(σ[1])")
        println("allocation: $(μ[2]) ± $(σ[2])")
        println("gc time:    $(μ[3]) ± $(σ[3])")
    end
end

uniquebench.jl

### ranges ###

julia> @stats x=1:2^15 unique(x)
time:       3.7159375e-7 ± 4.013905975041092e-8
allocation: 0.0 ± 0.0
gc time:    0.0 ± 0.0


### associative collections ###

julia> @stats s=Set(rand(Int64,2^15)) unique(s)
time:       0.0009458752031249999 ± 0.0003434409454841642
allocation: 262240.0 ± 0.0
gc time:    0.0 ± 0.0

julia> @stats is=IntSet(collect(1:2^15)) unique(is)
time:       0.0009920913906249999 ± 0.00044799769109717927
allocation: 262240.0 ± 0.0
gc time:    0.0 ± 0.0

julia> @stats d=Dict(zip(collect(1:2^15),rand(Int64,2^15))) unique(d)
time:       0.0009264861328125 ± 0.0004584347754295683
allocation: 524384.0 ± 0.0
gc time:    2.8284089843750002e-5 ± 0.0002637569369516736


### arrays with small eltypes ###

julia> @stats x=rand(Bool,2^18) unique(x)
time:       0.00019525041796875 ± 0.0003693199716505484
allocation: 262256.0 ± 0.0
gc time:    5.798378515625e-5 ± 0.00037538094090833673

julia> @stats x=rand(UInt8,2^18) unique(x)
time:       0.00016099780078125 ± 8.348396199362076e-5
allocation: 262256.0 ± 0.0
gc time:    0.0 ± 0.0

julia> @stats x=rand(Int8,2^18) unique(x)
time:       0.00014154846484374997 ± 6.900272968256993e-5
allocation: 262256.0 ± 0.0
gc time:    0.0 ± 0.0


### Arrays with structural zeros ###

julia> @stats A=Bidiagonal(collect(1:2^8),collect(2:2^8),true) unique(A)
time:       8.592412499999999e-5 ± 0.0004333578596779919
allocation: 38976.0 ± 0.0
gc time:    2.6895125e-5 ± 0.00043032199999999996

julia> @stats A=Diagonal(collect(1:2^8)) unique(A)
time:       0.00013670909765625 ± 0.0013926349651035386
allocation: 27896.33203125 ± 6789.3125
gc time:    6.05116953125e-5 ± 0.0009681871250000008

julia> @stats A=SymTridiagonal(collect(1:2^8),collect(2:2^8)) unique(A)
time:       0.0025976334140625 ± 0.0005780271678318505
allocation: 22048.0 ± 0.0
gc time:    0.0 ± 0.0

julia> @stats A=Tridiagonal(collect(2:2^8),collect(1:2^8),collect(2:2^8)) unique(A)
time:       5.9102359375e-5 ± 2.468377157018689e-5
allocation: 39040.0 ± 0.0
gc time:    0.0 ± 0.0

julia> @stats A=sparse(Diagonal(collect(1:2^8))) unique(A)
time:       6.676669140624998e-5 ± 6.50257387774067e-5
allocation: 23360.0 ± 0.0
gc time:    0.0 ± 0.0

julia> @stats A=sparse(collect(Diagonal(collect(1:2^8)))) unique(A)
time:       0.000114723140625 ± 4.7476982803060564e-5
allocation: 34960.0 ± 0.0
gc time:    0.0 ± 0.0