From fab59871976bdee8db52026025b3f798fb219c23 Mon Sep 17 00:00:00 2001 From: Shuhei Kadowaki Date: Thu, 6 Apr 2023 19:44:29 +0900 Subject: [PATCH] add heavy compilation benchmarks This commit adds more real-world, heavy compilation benchmarks, using DataFrames.jl, CSV.jl, Plots.jl and OrdinaryDiffEq.jl as examples. replaces #252 closes #251 --- .gitignore | 1 + LocalPreferences.toml | 4 + Manifest.toml | 1540 +++++++++++++++++++++++++- Project.toml | 4 + src/inference/InferenceBenchmarks.jl | 68 +- 5 files changed, 1583 insertions(+), 34 deletions(-) create mode 100644 LocalPreferences.toml diff --git a/.gitignore b/.gitignore index 8c960ec8..3db57b55 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ *.jl.cov *.jl.*.cov *.jl.mem +!LocalPreferences.toml diff --git a/LocalPreferences.toml b/LocalPreferences.toml new file mode 100644 index 00000000..203b6967 --- /dev/null +++ b/LocalPreferences.toml @@ -0,0 +1,4 @@ +# NOTE These packages are only used for the inference benchmark, +# thus they really don't need to be precompiled. +[SnoopPrecompile] +skip_precompile = ["CSV", "DataFrames", "OrdinaryDiffEq", "Plots"] diff --git a/Manifest.toml b/Manifest.toml index 0a10b8a8..6838fd08 100644 --- a/Manifest.toml +++ b/Manifest.toml @@ -1,54 +1,986 @@ # This file is machine-generated - editing it directly is not advised +[[Adapt]] +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "cc37d689f599e8df4f464b2fa3870ff7db7492ef" +uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" +version = "3.6.1" +weakdeps = ["StaticArrays"] + + [Adapt.extensions] + AdaptStaticArraysExt = "StaticArrays" + +[[ArgTools]] +uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" +version = "1.1.1" + +[[ArnoldiMethod]] +deps = ["LinearAlgebra", "Random", "StaticArrays"] +git-tree-sha1 = "62e51b39331de8911e4a7ff6f5aaf38a5f4cc0ae" +uuid = "ec485272-7323-5ecc-a04f-4719b315124d" +version = "0.2.0" + +[[ArrayInterface]] +deps = ["Adapt", "LinearAlgebra", "Requires", "SnoopPrecompile", "SparseArrays", 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"f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" +version = "1.3.7+1" + +[[nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.52.0+0" + +[[p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+0" + +[[x264_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" +uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" +version = "2021.5.5+0" + +[[x265_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" +uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+0" diff --git a/Project.toml b/Project.toml index 82a6a8cc..553ef601 100644 --- a/Project.toml +++ b/Project.toml @@ -3,10 +3,14 @@ uuid = "d7f09723-0a21-57e2-b9ef-316b714b6879" [deps] BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" REPL = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" diff --git a/src/inference/InferenceBenchmarks.jl b/src/inference/InferenceBenchmarks.jl index 6fb241f5..1562d0c5 100644 --- a/src/inference/InferenceBenchmarks.jl +++ b/src/inference/InferenceBenchmarks.jl @@ -54,8 +54,7 @@ struct InferenceBenchmarker <: AbstractInterpreter compress::Bool = true, discard_trees::Bool = true, inf_cache::Vector{InferenceResult} = InferenceResult[], - code_cache::InferenceBenchmarkerCache = InferenceBenchmarkerCache(IdDict{MethodInstance,CodeInstance}()), - ) + code_cache::InferenceBenchmarkerCache = InferenceBenchmarkerCache(IdDict{MethodInstance,CodeInstance}())) return new( world, inf_params, @@ -182,27 +181,9 @@ function opt_call(@nospecialize(f), @nospecialize(types = Base.default_tt(f)); end end -function tune_benchmarks!( - g::BenchmarkGroup; - seconds=30, - gcsample=true, - ) - for v in values(g) - v.params.seconds = seconds - v.params.gcsample = gcsample - v.params.evals = 1 # `setup` must be functional - end -end - # "inference" benchmark targets # ============================= -# TODO add TTFP? -# XXX some targets below really depends on the compiler implementation itself -# (e.g. `abstract_call_gf_by_type`) and thus a bit more unreliable -- ideally -# we want to replace them with other functions that have the similar characteristics -# but whose call graph are orthogonal to the Julia's compiler implementation - using REPL broadcasting(xs, x) = findall(>(x), abs.(xs)) let # check the compilation behavior for a function with lots of local variables @@ -294,11 +275,38 @@ let # check performance of opaque closure handling end end +using DataFrames, CSV, Plots, OrdinaryDiffEq + +function lorenz(du, u, p, t) + du[1] = 10.0(u[2] - u[1]) + du[2] = u[1] * (28.0 - u[3]) - u[2] + du[3] = u[1] * u[2] - (8 / 3) * u[3] +end +let p = ODEProblem(lorenz, [1.0; 0.0; 0.0], (0.0, 1.0)) + global prob::typeof(p) = p +end + +function tune_benchmarks!( + g::BenchmarkGroup; + seconds=30, + gcsample=true) + default = BenchmarkTools.DEFAULT_PARAMETERS + for v in values(g) + if v.params.seconds == default.seconds + v.params.seconds = seconds + end + if v.params.gcsample == default.gcsample + v.params.gcsample = gcsample + end + v.params.evals = 1 # `setup` must be functional + end +end + const SUITE = BenchmarkGroup() let g = addgroup!(SUITE, "abstract interpretation") - g["sin(42)"] = @benchmarkable (@abs_call sin(42)) - g["rand(Float64)"] = @benchmarkable (@abs_call rand(Float64)) + g["sin(42)"] = @benchmarkable @abs_call sin(42) + g["rand(Float64)"] = @benchmarkable @abs_call rand(Float64) g["println(::QuoteNode)"] = @benchmarkable (abs_call(println, (QuoteNode,))) g["broadcasting"] = @benchmarkable abs_call(broadcasting, (Vector{Float64},Float64)) g["REPL.REPLCompletions.completions"] = @benchmarkable abs_call( @@ -310,6 +318,10 @@ let g = addgroup!(SUITE, "abstract interpretation") g["many_global_refs"] = @benchmarkable abs_call(many_global_refs, (Int,)) g["many_invoke_calls"] = @benchmarkable abs_call(many_invoke_calls, (Vector{Float64},)) g["many_opaque_closures"] = @benchmarkable abs_call(many_opaque_closures, (Vector{Float64},)) + g["DataFrames.DataFrame(::Dict{Symbol,Any})"] = @benchmarkable abs_call(DataFrame, (Dict{Symbol,Any},)) + g["CSV.read(::String, DataFrame)"] = @benchmarkable (@abs_call CSV.read("some.csv", DataFrame)) seconds=70 + g["Plots.plot(::Matrix{Float64})"] = @benchmarkable (@abs_call plot(rand(10,3))) seconds=100 + g["OrdinaryDiffEq.solve(prob::ODEProblem, QNDF())"] = @benchmarkable @abs_call solve(prob, QNDF()) tune_benchmarks!(g) end @@ -327,12 +339,16 @@ let g = addgroup!(SUITE, "optimization") g["many_global_refs"] = @benchmarkable f() (setup = (f = opt_call(many_global_refs, (Int,)))) g["many_invoke_calls"] = @benchmarkable f() (setup = (f = opt_call(many_invoke_calls, (Vector{Float64},)))) g["many_opaque_closures"] = @benchmarkable f() (setup = (f = opt_call(many_opaque_closures, (Vector{Float64},)))) + g["DataFrames.DataFrame(::Dict{Symbol,Any})"] = @benchmarkable f() = (setup = (f = opt_call(DataFrame, (Dict{Symbol,Any},)))) + g["CSV.read(::String, DataFrame)"] = @benchmarkable f() = (setup = (f = @opt_call CSV.read("some.csv", DataFarme))) + g["Plots.plot(::Matrix{Float64})"] = @benchmarkable f() = (setup = (f = @opt_call plot(rand(10,3)))) + g["OrdinaryDiffEq.solve(prob::ODEProblem, QNDF())"] = @benchmarkable f() = (setup = (f = @opt_call solve(prob, QNDF()))) tune_benchmarks!(g) end let g = addgroup!(SUITE, "allinference") - g["sin(42)"] = @benchmarkable (@inf_call sin(42)) - g["rand(Float64)"] = @benchmarkable (@inf_call rand(Float64)) + g["sin(42)"] = @benchmarkable @inf_call sin(42) + g["rand(Float64)"] = @benchmarkable @inf_call rand(Float64) g["println(::QuoteNode)"] = @benchmarkable (inf_call(println, (QuoteNode,))) g["broadcasting"] = @benchmarkable inf_call(broadcasting, (Vector{Float64},Float64)) g["REPL.REPLCompletions.completions"] = @benchmarkable inf_call( @@ -344,6 +360,10 @@ let g = addgroup!(SUITE, "allinference") g["many_global_refs"] = @benchmarkable inf_call(many_global_refs, (Int,)) g["many_invoke_calls"] = @benchmarkable inf_call(many_invoke_calls, (Vector{Float64},)) g["many_opaque_closures"] = @benchmarkable inf_call(many_opaque_closures, (Vector{Float64},)) + g["DataFrames.DataFrame(::Dict{Symbol,Any})"] = @benchmarkable inf_call(DataFrame, (Dict{Symbol,Any},)) + g["CSV.read(::String, DataFrame)"] = @benchmarkable (@inf_call CSV.read("some.csv", DataFrame)) + g["Plots.plot(::Matrix{Float64})"] = @benchmarkable (@inf_call plot(rand(10,3))) seconds=60 + g["OrdinaryDiffEq.solve(prob::ODEProblem, QNDF())"] = @benchmarkable (@inf_call solve(prob, QNDF())) seconds=40 tune_benchmarks!(g) end