Large performance regression for simple bisection function

[giordano]

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 = f(mid) > 0 ? (left, mid) : (mid, right)
        ϵ₁ = right - left
    end
    (left + right)/2
end

on v1.6:

julia> @benchmark bisectroot(x -> x^2-7, 0, 7)
BenchmarkTools.Trial: 10000 samples with 935 evaluations.
 Range (min … max):  105.705 ns … 293.216 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     111.143 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   114.704 ns ±  12.841 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▄▂█▆▅▇▅▄   ▄▃▁▂       ▁▁                                      ▂
  ████████████████████████▇▇▇▆▇▇▇█▇▇▅▅▅▅▄▅▆▇▆▅▇▅▅▄▆▅▆▇▅▅▅▅▄▄▅▆▆ █
  106 ns        Histogram: log(frequency) by time        176 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

on master (a11de31):

julia> @benchmark bisectroot(x -> x^2-7, 0, 7)
BenchmarkTools.Trial: 10000 samples with 7 evaluations.
 Range (min … max):  4.255 μs … 308.235 μs  ┊ GC (min … max): 0.00% … 97.95%
 Time  (median):     4.441 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   5.046 μs ±   5.829 μs  ┊ GC (mean ± σ):  2.50% ±  2.19%

  ▃█▆▃▂▃▁▁▁▁▄▃▂ ▁▁▁▁                                          ▁
  ███████████████████▇▆▆▇▆▇▆▆▇██▇▆▆▅▅▆▅▆▆▆▅▅▆▅▅▆▅▅▅▆▅▅▄▅▅▅▅▅▄ █
  4.26 μs      Histogram: log(frequency) by time      10.5 μs <

 Memory estimate: 3.30 KiB, allocs estimate: 140.

Note that on master the function allocates, when it shouldn't.

The regression can be completely cleared (master would be actually ~25% faster) by replacing left, right = f(mid) > 0 ? (left, mid) : (mid, right) with an if like this:

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
        if f(mid) > 0
            left, right = left, mid
        else
            left, right = mid, right
        end
        ϵ₁ = right - left
    end
    (left + right)/2
end

v1.6:

julia> @benchmark bisectroot(x -> x^2-7, 0, 7)
BenchmarkTools.Trial: 10000 samples with 930 evaluations.
 Range (min … max):  105.654 ns … 240.912 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     111.938 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   115.669 ns ±  14.069 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▄ █▃█▅▃ ▃ ▄▃▂▁ ▁▁▁                                            ▂
  █▇█████▇███████████████▇▆▇▇▇▇▇▆▇▆▆▆▅▆▆▆▆█▆▆▆▆▆▆▆▇▆▆▅▅▅▆▆▄▅▃▅▄ █
  106 ns        Histogram: log(frequency) by time        183 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

master:

julia> @benchmark bisectroot(x -> x^2-7, 0, 7)
BenchmarkTools.Trial: 10000 samples with 969 evaluations.
 Range (min … max):  75.507 ns … 200.540 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     78.062 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   82.220 ns ±  10.800 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▂ █ ▇▁   ▂▂ ▁▂▁                      ▁                       ▁
  █▄████▅▇▆███████▇▇▇▇▇▇▇▇██▆▅▆▆▅▅▆▆▇▆▅█▅▅▅▄▅▄▆▆▆▄▃▅▄▅▄▃▅▄▅▄▄▄ █
  75.5 ns       Histogram: log(frequency) by time       135 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

At the moment I can't run git bisect myself to try and find the culprit (would take too long...), but if someone has clues about potential candidates, I could do a smaller search.

[KristofferC]

Looks regressed in 1.7-beta already.

The performance difference goes away if the call is made type stable: bisectroot(x -> x^2-7, 0.0, 7.0).

[giordano]

The performance difference goes away if the call is made type stable: bisectroot(x -> x^2-7, 0.0, 7.0).

Ugh, that's an excellent point. I thought about type-instability because of the unexpected memory allocations, I completely overlooked the fact the input arguments were reused in the body of the function, but I did use @code_warntype and it didn't flag anything:

julia> @code_warntype bisectroot(x -> x^2-7, 0, 7)
MethodInstance for bisectroot(::var"#19#20", ::Int64, ::Int64)
  from bisectroot(f::Function, left, right) in Main at /tmp/foo.jl:1
Arguments
  #self#::Core.Const(bisectroot)
  f::Core.Const(var"#19#20"())
  left::Int64
  right::Int64
Body::Float64
1 ─ %1 = (#self#)(f, left, right, 1.0e-6)::Float64
└──      return %1

A type-unstable call is probably borderline in terms of what's expected to be optimised, right?

[JeffBezanson]

I'm certainly curious to find out what caused this.

[KristofferC]

but I did use @code_warntype and it didn't flag anything:

It's because of the default argument. Use cthulhu to dig deeper into the calls.

[martinholters]

On 1.6, inference gives Tuple{Union{Float64, Int64}, Union{Float64, Int64}} for the tuple that gets destructured into left, right, while on master, it gives Union{Tuple{Float64, Int64}, Tuple{Union{Float64, Int64}, Float64}}. Note that master even produces a slightly tighter result (ruling out Tuple{Int64, Int64}), but the call to indexed_iterate then is done via dynamic dispatch, while 1.6 handles it more gracefully.

[maleadt]

Bisected:

3635f0473dea7c7254d611e3eaa939e0f6dd9484 is the first bad commit
commit 3635f0473dea7c7254d611e3eaa939e0f6dd9484
Author: Takafumi Arakaki <aka.tkf@gmail.com>
Date:   Mon Mar 15 12:28:21 2021 -0700

    Check issimpleenoughtype before stripping off type parameters in tmerge (#39980)

VERSION = v"1.7.0-DEV.710"
BenchmarkTools.Trial: 10000 samples with 9 evaluations.
 Range (min … max):  2.404 μs … 516.281 μs  ┊ GC (min … max): 0.00% … 98.92%
 Time  (median):     2.549 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.847 μs ±  10.815 μs  ┊ GC (mean ± σ):  9.24% ±  2.43%

vs

commit 11016bfcb2ec0ec3006eca4b556f9f5f24f2be71 (HEAD)
Author: Keno Fischer <keno@juliacomputing.com>
Date:   Mon Mar 15 10:55:35 2021 -0400

    Fix opaque closure codegen ABI (#40001)

VERSION = v"1.7.0-DEV.709"
BenchmarkTools.Trial: 10000 samples with 971 evaluations.
 Range (min … max):  75.457 ns … 87.332 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     76.703 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   76.605 ns ±  0.539 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

cc @tkf

[vtjnash]

The tmerge here is a slightly odd. While true it is exactly equal to the Union of the 2 input types, it is an awkward way to express the 3 Tuple elements, and so the optimizer later struggles to split this well in SROA.

8 ──       (@_11 = Core.tuple(left@_9, mid))::Tuple{Union{Float64, Int64}, Float64}
└───       goto #10
9 ──       (@_11 = Core.tuple(mid, right@_10))::Tuple{Float64, Union{Float64, Int64}}
10 ┄ %25 = @_11::Union{Tuple{Float64, Int64}, Tuple{Union{Float64, Int64}, Float64}}

This means codegen sees this type: Tuple{Union{Float64, Int64}, Union{Float64, Int64}}, which it doesn't support unboxed.