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using BenchmarkTools
using DataFrames
using Transducers
using VegaLite
resultpath = joinpath(@__DIR__, "result.json")
result, = BenchmarkTools.load(resultpath)
df_raw =
BenchmarkTools.leaves(result) |>
Map() do ((basesize, needleloc, ex), trial)
(
basesize = parse(Int, basesize),
needleloc = parse(Int, needleloc),
executor = Symbol(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
#-
begin
df = transform(groupby(df_stats, [:basesize, :needleloc, :time_stat])) do group
baseline = only(eachrow(filter(:executor => ==(:ThreadedEx), group)))
(speedup = baseline.time_ns ./ group.time_ns,)
end
filter!(:executor => !(==(:ThreadedEx)), df)
df
end
#-
plt1 = @vlplot(
facet = {row = {field = :executor, type = :nominal}, column = {field = :time_stat, type = :nominal}},
spec = {
layer = [
{
mark = {type = :line, point = true},
encoding = {
x = {field = :needleloc, scale = {type = :log, base = 2}},
y = {field = :speedup, axis = {title = "Speedup (T_default / T_\$executor)"}},
color = {field = :basesize, type = :nominal},
},
},
{
mark = :rule,
encoding = {y = {datum = 1}},
},
],
},
data = df,
)
plt2 = @vlplot(
mark = {type = :line, point = true},
x = {field = :needleloc, scale = {type = :log, base = 2}},
y = {field = :time_ns, axis = {title = "Time [ns]"}},
color = {field = :basesize, type = :nominal},
row = :executor,
column = :time_stat,
resolve = {scale = {y = "independent"}},
data = df_stats,
)
plt3 = @vlplot(
mark = {type = :line, point = true, clip = true},
x = {field = :needleloc, scale = {type = :log, base = 2}},
y = {field = :time_ns, axis = {title = "Time [ns]"}, scale = {domain = [0, 2_500_000]}},
color = {field = :basesize, type = :nominal},
row = :executor,
column = :time_stat,
data = df_stats,
)
nothing
# ## Notes
#
# #### Summary
#
# Peformance of parallel `findfirst` with different scheduling strategies
# ("executors") are benchmarked. `DepthFirstEx` is useful when the latency of
# the "hit case" is important and/or the needle is expected to be at somehwere
# in the beginning of the collection. Otherwise, `ThreadedEx` (default) is a
# good option. For consistent latency, `WorkStealingEx` may be useful.
#
# #### Benchmarked problem
#
# ```julia
# const DATA_LENGTH = 2^23
# xs = rand(DATA_LENGTH)
# xs[needleloc] = 2
# @benchmarkable(Folds.findfirst(>(1), xs, $Executor(basesize = $basesize)))
# ```
#
# #### Speedup
plt1
# Speedup of `DepthFirstEx` (first row) and `WorkStealingEx` (second row) with
# respect to the default `ThreadedEx` executor.
#
# Since **the x axis logarithmic**, note that the range of large speedup for
# `DepthFirstEx` is overly emphasized; i.e., `DepthFirstEx` does _not_ perform
# better on average if the location of the needle is expected to be uniformly
# distributed. On the other hand, `DepthFirstEx` is a good choice when it is
# important to finish fast when the reduction can be terminated early.
#
# `WorkStealingEx` seems to perform better in some limited range.
# #### Run-time
plt2
# **NOTE**: x axis logarithmic.
#
# * `DepthFirstEx` behaves consistently well up until the location of the needle
# is around the `basesize`.
#
# * The run-time of `ThreadedEx` (middle row) shows drastically different
# shape of when the median (left) or the minimum (right) is plotted. This
# is likely due to the randomization nature of Julia's scheduling (as of v1.5).
#
# * The peformance of `WorkStealingEx` is more consistent than `ThreadedEx`.
# #### Run-time (zoom)
plt3
# **NOTE**: x axis is logarithmic.
#
# Same as above (`plt2`) but with the range of Y axis fixed for all plots.
using BenchmarkTools
using Folds
using FoldsThreads
const SUITE = BenchmarkGroup()
const DATA_LENGTH = 2^23
for log2_basesize in 12:14
basesize = 2^log2_basesize
SUITE[basesize] = s1 = BenchmarkGroup()
for log2_needleloc in 11:23
needleloc = 2^log2_needleloc
s1[needleloc] = s2 = BenchmarkGroup()
xs = rand(DATA_LENGTH)
xs[needleloc] = 2
s2["ThreadedEx"] =
@benchmarkable( Folds.findfirst(>(1), $xs, ThreadedEx(basesize = $basesize)))
s2["WorkStealingEx"] =
@benchmarkable(Folds.findfirst(>(1), $xs, WorkStealingEx(basesize = $basesize)))
s2["DepthFirstEx"] =
@benchmarkable(Folds.findfirst(>(1), $xs, DepthFirstEx(basesize = $basesize)))
#=
s2["SpawnAllEx"] =
@benchmarkable(Folds.findfirst(>(1), $xs, SpawnAllEx(basesize = $basesize)))
=#
end
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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[[Crayons]]
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[[DataFrames]]
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git-tree-sha1 = "b0db5579803eabb33f1274ca7ca2f472fdfb7f2a"
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[[DataStructures]]
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[[Distributed]]
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[[FileIO]]
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version = "1.4.5"
[[FilePaths]]
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[[Folds]]
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[[FoldsThreads]]
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repo-rev = "master"
repo-url = "https://github.com/JuliaFolds/FoldsThreads.jl.git"
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version = "0.1.0"
[[Formatting]]
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[[HTTP]]
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[[InitialValues]]
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