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using BenchmarkTools
using DataFrames
using Statistics
using Transducers
using VegaLite
resultpath = joinpath(@__DIR__, "result.json")
result, = BenchmarkTools.load(resultpath)
df_raw =
BenchmarkTools.leaves(result) |>
Map() do ((nworks, ex), trial)
(nworks = parse(Int, nworks), executor = ex, trial = trial)
end |>
DataFrame
#-
begin
df_tmp = select(df_raw, Not(:trial))
df_tmp[!, :minimum] = map(trial -> minimum(trial).time, df_raw.trial)
df_tmp[!, :median] = map(trial -> median(trial).time, df_raw.trial)
df_tmp[!, :memory] = map(trial -> trial.memory, df_raw.trial)
df_stats = stack(
df_tmp,
[:minimum, :median],
variable_name = :time_stat,
value_name = :time_ns,
)
end
#-
df = combine(groupby(df_stats, [:nworks, :time_stat])) do group
d = Dict(zip(group.executor, group.time_ns))
(speedup = d["ThreadedEx"] / d["WorkStealingEx"],)
end
#-
plt1 = @vlplot(
layer = [
{
mark = {type = :line, point = true},
encoding = {
x = {field = :nworks},
y = {field = :speedup, axis = {title = "Speedup (T_default / T_WS)"}},
color = {field = :time_stat},
},
},
{
mark = :rule,
encoding = {y = {datum = 1}},
},
],
data = df,
width = 400,
height = 200,
)
plt2 = @vlplot(
mark = {type = :line, point = true},
x = :nworks,
y = {field = :time_ns, axis = {title = "Time [ns]"}},
color = {field = :time_stat},
column = :executor,
data = df_stats,
)
nothing
# ## Notes
#
# #### Summary
#
# Peformance of parallel reduce with wildly skewed run-time distribution is
# benchmarked with the default `ThreadedEx` executor and `WorkStealingEx`
# executor. `WorkStealingEx` consistently performces better than `ThreadedEx`
# (if the run-time distribution is unbalanced enough). Furthermore, the
# run-time of `WorkStealingEx` is much more conisstent than `ThreadedEx`.
#
# #### Benchmarked problem
#
# The following problem is benchmarked
#
# ```julia
# xs = 1:2^13
# Folds.sum($f, xs, $Executor(basesize = 1))
# ```
#
# where `f` spins for 100 μs for `nworks` items (i.e., every `length(xs) ÷
# nworks`) in the input collection `xs`.
#
# #### Speedup
plt1
# For this range of parameter, `WorkStealingEx` performs better than
# `ThreadedEx`. Note that the run-time distribution is _very_ skewed. The
# largest tried `nworks` is 50; i.e., only `50 /2^13 * 100 = 0.6` % of the
# items are actually compute-intensive.
# #### Run-time
plt2
# Note the big difference in the minimum and median run-time of `ThreadedEx`
# (left). This is probably due to the randomization by Julia's paralel task
# runtime. On the other hand, the performance of `WorkStealingEx` (right) is
# more consistent (smaller difference between minimum and median).
using BenchmarkTools
using Folds
using FoldsThreads
const SUITE = BenchmarkGroup()
const DATA_LENGTH = 2^13
function unbalanced_work(nworks, data_length)
if nworks == 0
space = typemax(Int)
else
space = cld(data_length, nworks)
end
function work(i)
n = 0
if mod(i, space) == 0
# Sping for 100 μs
t = time_ns() + 100_0000
while t > time_ns()
n += 1
end
end
return n
end
end
for nworks in 0:5:51
f = unbalanced_work(nworks, DATA_LENGTH)
xs = 1:DATA_LENGTH
s1 = SUITE[nworks] = BenchmarkGroup()
s1["ThreadedEx"] =
@benchmarkable(Folds.sum($f, $xs, ThreadedEx(basesize = 1, stoppable = false)))
s1["WorkStealingEx"] = @benchmarkable(Folds.sum($f, $xs, WorkStealingEx(basesize = 1)))
end
JULIA = julia --startup-file=no
export JULIA_NUM_THREADS = 32
export JULIA_LOAD_PATH = @
export JULIA_PROJECT = $(PWD)
.PHONY: rebuild
analysis.ipynb: analysis.jl result.json
$(MAKE) rebuild
rebuild:
$(JULIA) -e 'using Literate; Literate.notebook("analysis.jl")'
result.json:
$(JULIA) runbenchmarks.jl $@
# This file is machine-generated - editing it directly is not advised
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[[Base64]]
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[[CategoricalArrays]]
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[[Crayons]]
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[[DataStructures]]
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[[DelimitedFiles]]
deps = ["Mmap"]
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[[Distributed]]
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[[FileIO]]
deps = ["Pkg"]
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[[FilePaths]]
deps = ["FilePathsBase", "Glob", "MacroTools", "Reexport", "URIParser"]
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repo-rev = "master"
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[[Future]]
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[[Glob]]
git-tree-sha1 = "4df9f7e06108728ebf00a0a11edee4b29a482bb2"
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[[HTTP]]
deps = ["Base64", "Dates", "IniFile", "MbedTLS", "Sockets", "URIs"]
git-tree-sha1 = "63055ee44b5c2b95ec1921edcf856c60124ff0c3"
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@info "Using $(Threads.nthreads()) threads"
resultpath, = ARGS
mkpath(dirname(resultpath))
using BenchmarkTools
include("benchmarks.jl")
_, time, = @timed result = run(SUITE; verbose = true)
BenchmarkTools.save(resultpath, result)
write(joinpath(dirname(resultpath), "time"), string(time))
140.603282209
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