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using BenchmarkTools | |
using PyCall | |
using DataFrames | |
using CSV | |
py"""import timeit | |
import numpy as np""" | |
py"""def py_time(f, number=1_000): | |
timer = timeit.Timer(f) | |
time = min(timer.repeat(number=number)) | |
return time/number | |
""" | |
py_time = py"py_time" | |
sizes = (100, 1000, 10_000, 100_000, 1_000_000) | |
results = DataFrame() | |
for size in sizes | |
@info "Testing size $size" | |
data1 = rand(size) | |
data2 = rand(size) | |
data3 = rand(size) | |
data1_py = py"np.random.rand($size)" | |
data2_py = py"np.random.rand($size)" | |
data3_py = py"np.random.rand($size)" | |
op = "addition" | |
timing = @belapsed $data1 .+ $data2 | |
push!(results, (sys="Julia", op=op, size=size, timing=timing)) | |
timing = py"py_time(lambda: $data1_py + $data2_py)" | |
push!(results, (sys="Numpy", op=op, size=size, timing=timing)) | |
op="multiplication" | |
timing = @belapsed $data1 .* $data2 | |
push!(results, (sys="Julia", op=op, size=size, timing=timing)) | |
timing = py"py_time(lambda: $data1_py * $data2_py)" | |
push!(results, (sys="Numpy", op=op, size=size, timing=timing)) | |
op="division" | |
timing = @belapsed $data1 ./ $data2 | |
push!(results, (sys="Julia", op=op, size=size, timing=timing)) | |
timing = py"py_time(lambda: $data1_py / $data2_py)" | |
push!(results, (sys="Numpy", op=op, size=size, timing=timing)) | |
op="3ops" | |
timing = @belapsed $data1 .* $data2 .+ $data3 | |
push!(results, (sys="Julia", op=op, size=size, timing=timing)) | |
timing = py"py_time(lambda: $data1_py * $data2_py + $data3_py)" | |
push!(results, (sys="Numpy", op=op, size=size, timing=timing)) | |
op="exp" | |
timing = @belapsed exp.($data1) | |
push!(results, (sys="Julia", op=op, size=size, timing=timing)) | |
timing = py"py_time(lambda: np.exp($data1_py))" | |
push!(results, (sys="Numpy", op=op, size=size, timing=timing)) | |
op="log" | |
timing = @belapsed log.($data1) | |
push!(results, (sys="Julia", op=op, size=size, timing=timing)) | |
timing = py"py_time(lambda: np.log($data1_py))" | |
push!(results, (sys="Numpy", op=op, size=size, timing=timing)) | |
op="element_sum" | |
timing = @belapsed sum($data1) | |
push!(results, (sys="Julia", op=op, size=size, timing=timing)) | |
timing = py"py_time(lambda: $data1_py.sum())" | |
push!(results, (sys="Numpy", op=op, size=size, timing=timing)) | |
end | |
CSV.write("timings.csv", results) | |
show(results, allrows=true) | |
## plotting (could be executed stand-alone) | |
using Plots | |
using DataFrames | |
using CSV | |
results = DataFrame(CSV.File("timings.csv")) | |
agg_timings = DataFrame() | |
for (key, data) in pairs(groupby(results, [:op, :size])) | |
ratio = first(filter(x-> x.sys=="Numpy", data).timing) / first(filter(x-> x.sys=="Julia", data).timing) | |
push!(agg_timings, (op=key.op, size=key.size, ratio=ratio)) | |
end | |
agg_timings | |
plt = plot(; xlabel="element size", ylabel="Julia speedup (runtime Numpy / Julia - 1)", xscale=:log, yscale=:log, leg=:topright) | |
for (key, data) in pairs(groupby(agg_timings, :op)) | |
plot!(plt, data.size, data.ratio, label=key.op) | |
end | |
savefig(plt, "timings.png") | |
plt | |
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