Benchmarks

The benchmarks below were run on Julia version 1.11.5.

Default example

X = [40 30 20 10; 35 50 100 75; 30 80 70 120; 20 30 40 50]
u = [150, 300, 400, 150]
v = [200, 300, 400, 100]
@benchmark ipf(X, [u, v])

BenchmarkTools.Trial: 10000 samples with 1 evaluation per sample.
 Range (min … max):  64.690 μs …  11.541 ms  ┊ GC (min … max): 0.00% … 98.39%
 Time  (median):     69.510 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   74.515 μs ± 152.318 μs  ┊ GC (mean ± σ):  3.37% ±  1.69%

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  ██████████████████████████▇████████████████████▇▇▇▇▆▆▆▄▆▆▅▅▄ █
  64.7 μs       Histogram: log(frequency) by time      98.9 μs <

 Memory estimate: 41.36 KiB, allocs estimate: 918.

Large contingency table

X = reshape(repeat(1:16, 625), 100, 100)
Y = reshape(repeat(1:5, 2000), 100, 100) + X
m = ArrayMargins(Y)
@benchmark ipf(X, m)

BenchmarkTools.Trial: 10000 samples with 1 evaluation per sample.
 Range (min … max):  154.508 μs …   3.140 ms  ┊ GC (min … max): 0.00% … 93.45%
 Time  (median):     165.382 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   181.752 μs ± 102.004 μs  ┊ GC (mean ± σ):  5.34% ±  8.72%

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  155 μs        Histogram: log(frequency) by time        759 μs <

 Memory estimate: 279.86 KiB, allocs estimate: 1011.

Six-dimensional contingency table

X = reshape(repeat(1:12, 100), 6, 4, 2, 5, 5)
Y = reshape(repeat(1:5, 240), 6, 4, 2, 5, 5) + X
m = ArrayMargins(Y)
@benchmark ipf(X, m)

BenchmarkTools.Trial: 9976 samples with 1 evaluation per sample.
 Range (min … max):  468.241 μs …  10.663 ms  ┊ GC (min … max): 0.00% … 94.31%
 Time  (median):     485.483 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   499.074 μs ± 223.238 μs  ┊ GC (mean ± σ):  1.81% ±  3.90%

    ▂██▄  ▁▆▇▅▂                                                  
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  468 μs           Histogram: frequency by time          574 μs <

 Memory estimate: 118.12 KiB, allocs estimate: 2035.