# Replicate @tturbo performance

**URL:** <https://discourse.julialang.org/t/replicate-tturbo-performance/86146>\
**Category:** Performance\
**Created:** [August 22, 2022, 4:57pm UTC](https://discourse.julialang.org/t/replicate-tturbo-performance/86146 "2022-08-22T16:57:07Z")\
**Posts on this page:** 4\
**Page:** 2

<div class="post-metadata">

**Author:** ![Elrod](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/elrod/32/22461_2.png) [@Elrod](https://discourse.julialang.org/u/Elrod)\
**Post date:** [August 23, 2022, 10:26am UTC](https://discourse.julialang.org/t/replicate-tturbo-performance/86146/21 "2022-08-23T10:26:54Z")

</div>

> [@nsajko](#):
>
> ust one small additional note: my above post seems to show that LoopVectorization doesn’t provide significant benefit over base Julia for the myminmax problem

This is because you’re totally memory bound. Try `@turbo` with vectors of length 1024 or less.

Did you start julia with multiple threads?

EDIT:  
Interestingly, I hadn’t actually seen LLVM SIMD min/max functions before, but it is now.  
LV should still do better for sizes like 255, i.e. the power of 2-1.

```julia
julia> using .Minmax

julia> x = se(6);

julia> @btime myminmax1_turbo($x)
  23.396 ns (0 allocations: 0 bytes)
(1, 64)

julia> @btime myminmax1_tturbo($x)
  33.715 ns (0 allocations: 0 bytes)
(1, 64)

julia> @btime myminmax1_basic($x)
  36.803 ns (0 allocations: 0 bytes)
(1, 64)

julia> @btime myminmax2_turbo($x)
  20.421 ns (0 allocations: 0 bytes)
(1, 64)

julia> @btime myminmax2_tturbo($x)
  23.642 ns (0 allocations: 0 bytes)
(1, 64)

julia> @btime myminmax2_basic($x)
  36.863 ns (0 allocations: 0 bytes)
(1, 64)

julia> @btime myminmax_mapreduce($x)
  40.438 ns (0 allocations: 0 bytes)
(1, 64)

```

Once upon a time, base Julia had trouble SIMDing this.

Still, LV is still a fair bit faster. Even with the threading check (these vectors are too small to thread).

I got to length 65k before I noticed LV using 2 threads.

---

<div class="post-metadata">

**Author:** ![nsajko](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nsajko/32/221187_2.png) [@nsajko](https://discourse.julialang.org/u/nsajko)\
**Post date:** [August 23, 2022, 11:13am UTC](https://discourse.julialang.org/t/replicate-tturbo-performance/86146/22 "2022-08-23T11:13:29Z")

</div>

Indeed you are correct. Comparing performance of `myminmax2_basic`, `myminmax2_turbo` and `myminmax2_tturbo` across different input lengths, `@turbo` wins for the smallest inputs, then in the range from `se(12)` to `se(13)``@tturbo` takes the lead, after that `myminmax2_basic` is tied with `myminmax2_turbo` but `myminmax2_tturbo` is suddenly much slower (comparing the median timings):

```julia
julia> @benchmark myminmax2_basic(i) setup = (Random.seed!(12345678); i = se(14))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max): 9.457 μs … 43.592 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 9.759 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 10.439 μs ± 1.824 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

  ▅█▆▅▄▄▃▃▃▃▃▃▂▂▂▂▂▂▂▂▁▁▁▁▁ ▁ ▂
  ████████████████████████████████▇▇▇▆▇▇▆▆▆▅▆▆▆▅▅▆▄▆▆▅▅▅▄▅▄▁▄ █
  9.46 μs Histogram: log(frequency) by time 17.3 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_turbo(i) setup = (Random.seed!(12345678); i = se(14))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max): 9.418 μs … 93.085 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 9.709 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 10.176 μs ± 2.005 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

  ▆█▆▅▅▄▄▃▂▂▂▂▁▁ ▂
  █████████████████▇▇█▆▆▆▆▅▆▆▅▅▅▅▅▆▅▅▄▅▅▅▅▄▅▄▃▁▃▄▅▅▅▄▄▃▁▄▃▁▁▄ █
  9.42 μs Histogram: log(frequency) by time 19.1 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_tturbo(i) setup = (Random.seed!(12345678); i = se(14))
BenchmarkTools.Trial: 9767 samples with 5 evaluations.
 Range (min … max): 6.448 μs … 26.342 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 12.241 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 12.036 μs ± 1.755 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

                               ▂▂▃▅▆█▅▃▁                       
  ▁▁▁▁▂▂▃▃▂▂▂▂▁▂▁▁▂▂▂▃▃▄▅▆▆▅▆▆██████████▇▆▄▃▂▂▃▂▂▂▁▁▁▁▁▁▁▁▁▁▁ ▃
  6.45 μs Histogram: frequency by time 16.9 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_basic(i) setup = (Random.seed!(12345678); i = se(15))
BenchmarkTools.Trial: 9742 samples with 1 evaluation.
 Range (min … max): 18.815 μs … 119.003 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 23.825 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 24.777 μs ± 4.714 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

      ▁▃▄▆█▇███▅▅▃▁▁                                            
  ▄▅▅▇██████████████▇▆▅▅▄▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▃
  18.8 μs Histogram: frequency by time 44.4 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_turbo(i) setup = (Random.seed!(12345678); i = se(15))
BenchmarkTools.Trial: 9707 samples with 1 evaluation.
 Range (min … max): 18.815 μs … 106.610 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 23.704 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 24.672 μs ± 4.239 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

        ▁▃▆▇█▇▆▅▄▃▁                                             
  ▁▂▃▄▃▆████████████▆▆▅▄▄▄▃▃▂▂▂▂▂▂▂▂▂▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▃
  18.8 μs Histogram: frequency by time 42 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_tturbo(i) setup = (Random.seed!(12345678); i = se(15))
BenchmarkTools.Trial: 5236 samples with 1 evaluation.
 Range (min … max): 13.044 μs … 130.655 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 43.958 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 44.657 μs ± 8.647 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

                            ▂▃▅▆█▆▆▃▂                           
  ▁▁▁▁▁▁▁▁▂▂▂▂▂▂▁▂▃▃▂▂▂▂▃▃▅▇███████████▇▆▅▅▄▄▃▂▃▂▁▂▂▁▁▁▁▁▁▁▂▃▁ ▃
  13 μs Histogram: frequency by time 72.2 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

```

This is with Julia started with four threads (same as above).

BTW, don’t think that I’m criticizing your work, I know that your packages are tremendously useful, it’s just that comparing microbenchmarks is something I enjoy very much 😁. Also thank you for being so instructive here.

---

<div class="post-metadata">

**Author:** ![Elrod](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/elrod/32/22461_2.png) [@Elrod](https://discourse.julialang.org/u/Elrod)\
**Post date:** [August 23, 2022, 12:19pm UTC](https://discourse.julialang.org/t/replicate-tturbo-performance/86146/23 "2022-08-23T12:19:36Z")

</div>

FWIW, I got

```julia
julia> using Random

julia> @benchmark myminmax2_basic(i) setup = (Random.seed!(12345678); i = se(14))
BenchmarkTools.Trial: 10000 samples with 6 evaluations.
 Range (min … max): 5.611 μs … 16.944 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 6.013 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 6.156 μs ± 802.595 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

   ▃▄▇█▆▃
  ████████▆▅▄▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▂▂ ▃
  5.61 μs Histogram: frequency by time 10.7 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_turbo(i) setup = (Random.seed!(12345678); i = se(14))
BenchmarkTools.Trial: 10000 samples with 8 evaluations.
 Range (min … max): 3.982 μs … 13.480 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 4.304 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 4.473 μs ± 687.470 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

  ▃█▂▆▇▅▂
  ████████▆▅▄▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▄▃▃▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂ ▃
  3.98 μs Histogram: frequency by time 8.16 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_tturbo(i) setup = (Random.seed!(12345678); i = se(14))
BenchmarkTools.Trial: 10000 samples with 9 evaluations.
 Range (min … max): 3.263 μs … 13.234 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 4.667 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 4.710 μs ± 791.557 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

   ▂▃▂▂▂▃▂▄▄▂▄▆█▇▂▃▁ ▁ ▁▂ ▁ ▁ ▂
  ▄██████████████████████▇▇█▇▅▄▆█▆▅████▆▆▆▄▅▄▆▇▆▆▅▄▄▃▃▁▁▁▁▃▃▆ █
  3.26 μs Histogram: log(frequency) by time 9.35 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_basic(i) setup = (Random.seed!(12345678); i = se(15))
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max): 17.183 μs … 67.228 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 22.326 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 23.755 μs ± 5.780 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

      ▂▆██▆▄▁
  ▂▂▃▆███████▇▆▅▄▄▄▃▃▃▃▂▂▂▂▂▂▂▁▂▂▂▂▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃
  17.2 μs Histogram: frequency by time 54.7 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_turbo(i) setup = (Random.seed!(12345678); i = se(15))
BenchmarkTools.Trial: 9785 samples with 3 evaluations.
 Range (min … max): 10.911 μs … 34.302 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 14.028 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 14.655 μs ± 2.598 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

         ▃▆█▇▄▁
  ▂▂▂▂▃▄▇██████▇▅▄▄▄▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃
  10.9 μs Histogram: frequency by time 28.6 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark myminmax2_tturbo(i) setup = (Random.seed!(12345678); i = se(15))
BenchmarkTools.Trial: 6380 samples with 7 evaluations.
 Range (min … max): 7.464 μs … 24.428 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 9.435 μs ┊ GC (median): 0.00%
 Time (mean ± σ): 9.811 μs ± 1.682 μs ┊ GC (mean ± σ): 0.00% ± 0.00%

         ▄▅█▇▂ ▂▅▃
  ▂▃▄▅▆▇██████▇████▄▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▁▂▂▁▁▂▁▁▁▂ ▃
  7.46 μs Histogram: frequency by time 18.1 μs <

 Memory estimate: 0 bytes, allocs estimate: 0.

```

So for `15` it helped, but I saw the regressed mean time for `14` as well.

Looking at only minimum times tends to be misleading for multithreaded or allocating code.

> it’s just that comparing microbenchmarks is something I enjoy very much

Feel free to make PRs that adjust the threading ramp up or heuristics.

---

<div class="post-metadata">

**Author:** ![gitboy16](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gitboy16/32/24906_2.png) [@gitboy16](https://discourse.julialang.org/u/gitboy16)\
**Post date:** [August 23, 2022, 12:53pm UTC](https://discourse.julialang.org/t/replicate-tturbo-performance/86146/24 "2022-08-23T12:53:40Z")

</div>

You should try with `x = rand(10^8)` or more and you should see a difference in your benchmarks.

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