Timothy (Aug 24 2021 at 11:18): Timothy (Aug 24 2021 at 11:18):Using @time, I'm seeing >100% compilation time, which is clearly quite suspect. What's going on? How can I get an accurate result?
julia> function bisectroot(f::Function, left, right, ϵ=1e-6)
@assert f(left) < 0 && f(right) > 0
ϵ₁ = Inf
while ϵ₁ > ϵ
mid = (left + right)/2
left, right = if f(mid) > 0 (left, mid) else (mid, right) end
ϵ₁ = right - left
end
(left + right)/2
end
bisectroot (generic function with 2 methods)
julia> t = @time bisectroot(x -> x^2-7, 0, 7)
0.032397 seconds (44.41 k allocations: 2.623 MiB, 106.97% compilation time)
2.6457510590553284
I believe it's explained in the docstring: https://docs.julialang.org/en/v1/base/base/#Base.@time
Timothy (Aug 24 2021 at 12:20):I may be missing the obvious, but I don't see what you're referring to. There's a bit about time not being counted, but that's compile time not execution time, and if I add @eval I see:
julia> t = @time @eval bisectroot(x -> x^2-7, 0, 7)
0.033370 seconds (44.56 k allocations: 2.634 MiB, 105.17% compilation time)
Maybe https://github.com/JuliaLang/julia/issues/41281?
Mosè Giordano (Aug 24 2021 at 17:46):Timothy said:
I may be missing the obvious, but I don't see what you're referring to. There's a bit about time not being counted, but that's compile time not execution time, and if I add
@evalI see:julia> t = @time @eval bisectroot(x -> x^2-7, 0, 7) 0.033370 seconds (44.56 k allocations: 2.634 MiB, 105.17% compilation time)
I was referring to @eval. The discussion which led to that comment in the docstring was about wrong compilation times, including over 100% compile time at some point
Mosè Giordano (Aug 24 2021 at 17:53):with master I get
julia> @time bisectroot(x -> x^2-7, 0, 7)
0.053943 seconds (74.62 k allocations: 4.158 MiB, 99.83% compilation time)
2.6457510590553284
so it's likely the issue Eric pointed to (fixed for Julia v1.7) above is related
Mosè Giordano (Aug 24 2021 at 18:31):BTW, in Julia v1.6 I get:
julia> @benchmark bisectroot(x -> x^2-7, 0, 7)
BenchmarkTools.Trial: 10000 samples with 935 evaluations.
Range (min … max): 105.914 ns … 208.436 ns ┊ GC (min … max): 0.00% … 0.00%
Time (median): 108.907 ns ┊ GC (median): 0.00%
Time (mean ± σ): 110.994 ns ± 11.000 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
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128 ns Histogram: frequency by time 117 ns <
Memory estimate: 0 bytes, allocs estimate: 0.
and master
julia> @benchmark bisectroot(x -> x^2-7, 0, 7)
BenchmarkTools.Trial: 10000 samples with 7 evaluations.
Range (min … max): 4.304 μs … 247.652 μs ┊ GC (min … max): 0.00% … 97.84%
Time (median): 4.431 μs ┊ GC (median): 0.00%
Time (mean ± σ): 4.807 μs ± 5.423 μs ┊ GC (mean ± σ): 2.50% ± 2.19%
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5.14 μs Histogram: frequency by time 4.47 μs <
Memory estimate: 3.30 KiB, allocs estimate: 140.
large regression?
Mosè Giordano (Aug 24 2021 at 18:32):and also these histograms show confusing numbers on the x-axis: https://github.com/JuliaCI/BenchmarkTools.jl/issues/249 maybe you could have a look? :wink:
Mosè Giordano (Aug 24 2021 at 18:51):I get about the same performance (~3 μs) between Julia v1.6 and master by replacing the if with ifelse, which is a modest improvement for master, still a large regression for v1.6
Timothy (Aug 25 2021 at 05:20):Ah, so this should be fixed with 1.7, that's good to know. That performance regression looks slightly concerning, I hope that's resolved before 1.7 is released. Oh, and I took a look at that BenchmarkTools issue :smiling_face:.
Mosè Giordano (Aug 25 2021 at 21:09):for the performance regression, Kristoffer pointed out that the call is type-unstable: if you use 0.0 and 7.0 as arguments, on master you recover the same performance as in v1.6
Last updated: Nov 22 2024 at 04:41 UTC