# Bad performance of anonymous functions even when they are type-stable

**URL:** <https://discourse.julialang.org/t/bad-performance-of-anonymous-functions-even-when-they-are-type-stable/108173>\
**Category:** Performance\
**Tags:** function, anonymous-function\
**Created:** [December 30, 2023, 4:03am UTC](https://discourse.julialang.org/t/bad-performance-of-anonymous-functions-even-when-they-are-type-stable/108173 "2023-12-30T04:03:51Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![frankwswang](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/frankwswang/32/18561_2.png) [@frankwswang](https://discourse.julialang.org/u/frankwswang)\
**Post date:** [December 30, 2023, 4:03am UTC](https://discourse.julialang.org/t/bad-performance-of-anonymous-functions-even-when-they-are-type-stable/108173/1 "2023-12-30T04:03:51Z")

</div>

MWE:

```julia
julia> f1 = function (x)
                x/2
            end
#3 (generic function with 1 method)

julia> function f1_2(x)
           x/2
       end
f1_2 (generic function with 1 method)

julia> f2 = function (x)
                x
            end
#5 (generic function with 1 method)

julia> function f2_2(x)
           x
       end
f2_2 (generic function with 1 method)

julia> using BenchmarkTools

julia> @benchmark f1(0.1)
BenchmarkTools.Trial: 10000 samples with 999 evaluations.
 Range (min … max): 16.517 ns … 360.561 ns ┊ GC (min … max): 0.00% … 90.23%
 Time (median): 19.019 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 20.266 ns ± 9.995 ns ┊ GC (mean ± σ): 0.44% ± 1.54%

     ▁ █
  ▂▄▆█▃▅▆▆█▃▂▃▄▄▄▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▂▂▁▂▂▂ ▃
  16.5 ns Histogram: frequency by time 34.9 ns <

 Memory estimate: 16 bytes, allocs estimate: 1.

julia> @benchmark f1_2(0.1)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 0.800 ns … 237.800 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 0.800 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 0.904 ns ± 3.317 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

  █ ▇ ▂ ▁
  █▁▁▁▁█▁▁▁▁█▁▁▁▁▁▄▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▇ █
  0.8 ns Histogram: log(frequency) by time 1.9 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark f2(0.1)
BenchmarkTools.Trial: 10000 samples with 999 evaluations.
 Range (min … max): 11.712 ns … 1.559 μs ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 13.614 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 15.054 ns ± 19.157 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

      ██ ▇▂
  ▂▃▄▆██▆▅██▅▄▄▂▂▂▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▄▂▃▂▂▂▂▂▁▂▂▁▂▂▂▂▂▂▂▂▂▂▂ ▃
  11.7 ns Histogram: frequency by time 27.8 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark f2_2(0.1)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 0.800 ns … 277.500 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 0.800 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 0.917 ns ± 3.086 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

  █ ▄
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  0.8 ns Histogram: frequency by time 1 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

```

Even though all functions are type-stable, the anonymous versions are drastically slower than the non-anonymous ones. Aren’t they supposed to be as generic as the latter?

BTW for both Julia 1.9.4 and 1.10.0 on my local machine, the same issue occurs.

Hence, if I have to generate functions on the fly (which is why I want to use anonymous functions), how can I achieve the same performance as the non-anonymous function versions, assuming I can ensure type stability?

Thanks!

---

<div class="post-metadata">

**Author:** ![mkitti](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mkitti/32/12459_2.png) [@mkitti](https://discourse.julialang.org/u/mkitti)\
**Post date:** [December 30, 2023, 5:13am UTC](https://discourse.julialang.org/t/bad-performance-of-anonymous-functions-even-when-they-are-type-stable/108173/2 "2023-12-30T05:13:25Z")

</div>

You are introducing type instability by not making the global anonymous functions const.

```julia
julia> f1 = function (x)
           x/2
       end
#3 (generic function with 1 method)

julia> function f1_2(x)
           x/2
       end
f1_2 (generic function with 1 method)

julia> const f1_const = function (x)
           x/2
       end
#5 (generic function with 1 method)

julia> using BenchmarkTools

julia> @benchmark f1(0.1)
BenchmarkTools.Trial: 10000 samples with 998 evaluations.
 Range (min … max): 15.499 ns … 816.685 ns ┊ GC (min … max): 0.00% … 94.69%
 Time (median): 15.917 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 16.896 ns ± 11.634 ns ┊ GC (mean ± σ): 1.11% ± 1.62%

   ▃▅█▇█▅▄▁▁ ▁ ▃▄▄▅▃▄▂▂ ▁▁▃▂▃▂▃▁▁ ▂
  ▇█████████▇▇▃▅▄▅▁▃▁▄▄▇████▇████████████████████▆█▇▇▆█▆▇▆▆▄▅▆ █
  15.5 ns Histogram: log(frequency) by time 20.2 ns <

 Memory estimate: 16 bytes, allocs estimate: 1.

julia> @benchmark f1_2(0.1)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 1.770 ns … 21.875 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 1.771 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 1.803 ns ± 0.264 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

   █ ▅
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  1.77 ns Histogram: frequency by time 1.82 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark f1_const(0.1)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 1.770 ns … 32.813 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 1.823 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 1.830 ns ± 0.443 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

  █ █
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  1.77 ns Histogram: frequency by time 1.98 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> isconst(Main, :f1)
false

julia> isconst(Main, :f1_2)
true

julia> isconst(Main, :f1_const)
true

```

Essentially global values are type unstable because their types can change at any time. Another solution is to introduce some kind of local scope.

```julia
julia> let f1_let = function (x)
               x/2
           end
           @benchmark $f1_let(1.2)
       end
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 1.406 ns … 64.479 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 1.458 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 1.470 ns ± 0.666 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

                              █▁
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  1.41 ns Histogram: frequency by time 1.51 ns <

julia> g() = begin
           f1_local = x->x/2
           @benchmark $f1_local(8.2)
       end
g (generic function with 1 method)

julia> g()
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 1.406 ns … 10.000 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 1.458 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 1.451 ns ± 0.139 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

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  1.41 ns Histogram: frequency by time 1.51 ns <

```

---

<div class="post-metadata">

**Author:** ![frankwswang](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/frankwswang/32/18561_2.png) [@frankwswang](https://discourse.julialang.org/u/frankwswang)\
**Post date:** [December 30, 2023, 6:56am UTC](https://discourse.julialang.org/t/bad-performance-of-anonymous-functions-even-when-they-are-type-stable/108173/3 "2023-12-30T06:56:46Z")

</div>

Thanks for the reply! This makes sense to me now. Even though the anonymous function has a specific type, when assigned to a global variable `f1` or `f2`, these variables are not type-stable because, unlike the function name for non-anonymous functions, they can be reassigned with other types of values.

Based on this understanding, I interpolated the value of `f1` before running the benchmark, and they became as performant as the non-anonymous counterparts:

```julia
julia> @benchmark ($f1)(0.1)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 0.800 ns … 126.300 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 0.900 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 1.113 ns ± 1.667 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

  █ █ ▁ ▁ ▆ ▂ ▂
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  0.8 ns Histogram: log(frequency) by time 2 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark ($f2)(0.1)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max): 0.800 ns … 196.200 ns ┊ GC (min … max): 0.00% … 0.00%
 Time (median): 0.800 ns ┊ GC (median): 0.00%
 Time (mean ± σ): 0.926 ns ± 2.960 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

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  0.8 ns Histogram: log(frequency) by time 1.2 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

```