cucountmap is a faster countmap equivalent utilizing CUDA.jl for Vector{T} where isbits(T) and sizeof(T) <= 2.
using CuCountMap
v = rand(Int16, 1_000_000)
cucountmap(v) # converts v to cu(v) and then run countmap
using CUDA: cu
cuv = cu(v)
countmap(cuv) # StatsBase.countmap is overloaded for CuArraysDict{Int16, Int64} with 65536 entries:
-23731 => 18
29965 => 13
30270 => 14
1703 => 17
7685 => 16
-7029 => 16
3406 => 12
-30706 => 18
28804 => 10
27640 => 14
-17985 => 18
-28261 => 12
-2851 => 19
2015 => 12
-25023 => 15
31375 => 16
-13631 => 15
-8219 => 19
28165 => 14
⋮ => ⋮
using CUDA
using CuCountMap
using StatsBase: countmap
v = rand(Int16, 10_000_000);
using BenchmarkTools
cpu_to_gpu_benchmark = @benchmark gpu_countmap = cucountmap($v)BenchmarkTools.Trial: 954 samples with 1 evaluation.
Range (min … max): 4.374 ms … 13.528 ms ┊ GC (min … max): 0.00% … 52.22%
Time (median): 4.814 ms ┊ GC (median): 0.00%
Time (mean ± σ): 5.151 ms ± 1.145 ms ┊ GC (mean ± σ): 3.24% ± 8.90%
██
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4.37 ms Histogram: frequency by time 11.3 ms <
Memory estimate: 2.38 MiB, allocs estimate: 103.
cpu_to_cpu_benchmark = @benchmark cpu_countmap = countmap($v)BenchmarkTools.Trial: 291 samples with 1 evaluation.
Range (min … max): 15.864 ms … 26.632 ms ┊ GC (min … max): 0.00% … 18.12
%
Time (median): 16.843 ms ┊ GC (median): 0.00%
Time (mean ± σ): 17.195 ms ± 1.444 ms ┊ GC (mean ± σ): 1.72% ± 5.17
%
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15.9 ms Histogram: frequency by time 22.6 ms <
Memory estimate: 4.17 MiB, allocs estimate: 37.
cuv = CUDA.cu(v)
gpu_to_gpu_benchmark = @benchmark gpu_countmap2 = countmap(cuv)BenchmarkTools.Trial: 2242 samples with 1 evaluation.
Range (min … max): 1.799 ms … 9.377 ms ┊ GC (min … max): 0.00% … 73.02
%
Time (median): 1.995 ms ┊ GC (median): 0.00%
Time (mean ± σ): 2.150 ms ± 643.115 μs ┊ GC (mean ± σ): 3.50% ± 8.43
%
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1.8 ms Histogram: frequency by time 5.82 ms <
Memory estimate: 2.38 MiB, allocs estimate: 134.
using Plots
using Statistics: mean
cpu_to_gpu = mean(cpu_to_gpu_benchmark.times)/1000/1000
gpu_to_gpu = mean(gpu_to_gpu_benchmark.times)/1000/1000
cpu_to_cpu = mean(cpu_to_cpu_benchmark.times)/1000/1000
plot(
["CPU Array on CPU \n countmap(v)", "convert CPU Array to GPU array on GPU \n cucountmap(cu(v))", "GPU array on GPU \n cucountmap(cuv)"],
[cpu_to_cpu, cpu_to_gpu, gpu_to_gpu],
seriestypes = :bar, title="CuCountMap.cucountmap vs StatsBase.countmap", label="ms",
legendtitle="Mean time")[ Info: Precompiling Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]
