From 5b32a12b621e9097c02273c227e9427b467eadff Mon Sep 17 00:00:00 2001 From: Carlo Lucibello Date: Fri, 25 Feb 2022 07:40:32 +0100 Subject: [PATCH 1/6] add benchmarks --- benchmark/Project.toml | 7 +++++ benchmark/benchmarks.jl | 57 ++++++++++++++++++++++++++++++++++++++ benchmark/result.json | 1 + benchmark/runbenchmarks.jl | 13 +++++++++ benchmark/tune.json | 1 + 5 files changed, 79 insertions(+) create mode 100644 benchmark/Project.toml create mode 100644 benchmark/benchmarks.jl create mode 100644 benchmark/result.json create mode 100644 benchmark/runbenchmarks.jl create mode 100644 benchmark/tune.json diff --git a/benchmark/Project.toml b/benchmark/Project.toml new file mode 100644 index 000000000..0a7fe3966 --- /dev/null +++ b/benchmark/Project.toml @@ -0,0 +1,7 @@ +[deps] +BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694" +Graphs = "093fc24a-ae57-5d10-9952-331d41423f4d" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +PkgBenchmark = "32113eaa-f34f-5b0d-bd6c-c81e245fc73d" diff --git a/benchmark/benchmarks.jl b/benchmark/benchmarks.jl new file mode 100644 index 000000000..714847f5b --- /dev/null +++ b/benchmark/benchmarks.jl @@ -0,0 +1,57 @@ +using SparseArrays +using GraphNeuralNetworks +using BenchmarkTools +import Random: seed! +using LinearAlgebra + +SUITE = BenchmarkGroup() + +seed!(17) +n = 1024 +A = sprand(n, n, 0.01) +v = rand(1, n) +X = rand(100, n) +w = reshape(A.nzval, 1, :) +e = rand(1, length(A.nzval)) + +g = GNNGraph(A, ndata=(; v, X), edata=(; e, w), graph_type=:coo) + +spmv(g) = propagate((xi, xj, e) -> e .* xj, g, +; xj=g.ndata.v, e=g.edata.w) +spmm1(g) = propagate((xi, xj, e) -> e .* xj , g, +; xj=g.ndata.X, e=g.edata.w) +spmm2(g) = propagate(e_mul_xj, g, +; xj=g.ndata.X, e=vec(g.edata.w)) + +@assert isequal(spmv(g), v * A) +@assert isequal(spmm1(g), X * A) +@assert isequal(spmm2(g), X * A) + +let sub = SUITE["e_mul_xj"] = BenchmarkGroup() + sub["spmv0"] = @benchmarkable $v * $A + sub["spmv"] = @benchmarkable spmv($g) + sub["spmm0"] = @benchmarkable $X * $A + sub["spmm1"] = @benchmarkable spmm1($g) + sub["spmm2"] = @benchmarkable spmm2($g) +end + +# function spmm_copyxj_fused(g) +# propagate( +# copy_xj, +# g, +; xj=g.ndata.B +# ) +# end + +# function spmm_copyxj_unfused(g) +# propagate( +# (xi, xj, e) -> xj, +# g, +; xj=g.ndata.B +# ) +# end + +# Adj = map(x -> x > 0 ? 1 : 0, A) +# @assert spmm_copyxj_unfused(g) ≈ B * Adj +# @assert spmm_copyxj_fused(g) ≈ B * Adj # bug fixed in #107 + +# @btime spmm_copyxj_fused(g) # 268.614 μs (22 allocations: 1.13 MiB) +# @btime spmm_copyxj_unfused(g) # 4.263 ms (52855 allocations: 12.23 MiB) +# @btime B * Adj # 196.135 μs (2 allocations: 800.05 KiB) + +# println() \ No newline at end of file diff --git a/benchmark/result.json b/benchmark/result.json new file mode 100644 index 000000000..a3a80ae62 --- /dev/null +++ b/benchmark/result.json @@ 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Version 1.7.1\nCommit ac5cc99908 (2021-12-22 19:35 UTC)\nPlatform Info:\n OS: Linux (x86_64-pc-linux-gnu)\n \"Manjaro Linux\"\n uname: Linux 5.16.7-1-MANJARO #1 SMP PREEMPT Sun Feb 6 12:22:29 UTC 2022 x86_64 unknown\n CPU: Intel(R) Core(TM) i7-10710U CPU @ 1.10GHz: \n speed user nice sys idle irq\n #1-12 1600 MHz 20611 s 1955 s 4737 s 457227 s 4328 s\n \n Memory: 15.464141845703125 GB (4237.3046875 MB free)\n Uptime: 32039.11 sec\n Load Avg: 0.84 0.67 0.82\n WORD_SIZE: 64\n LIBM: libopenlibm\n LLVM: libLLVM-12.0.1 (ORCJIT, skylake)\n","commit":"77f0e0f6807636163eea239b03e8840e9916dc0f","julia_commit":"ac5cc99908d463582e66db3368b9b48fae1e2525","date":"2022-02-25T07:36:06.729"} \ No newline at end of file diff --git a/benchmark/runbenchmarks.jl b/benchmark/runbenchmarks.jl new file mode 100644 index 000000000..30a4458b8 --- /dev/null +++ b/benchmark/runbenchmarks.jl @@ -0,0 +1,13 @@ +using PkgBenchmark + +benchmarkpkg( + dirname(@__DIR__), + BenchmarkConfig( + id = "v0.3.13", + env = Dict( + "JULIA_NUM_THREADS" => "1", + "OMP_NUM_THREADS" => "1", + ), + ), + resultfile = joinpath(@__DIR__, "result.json"), +) diff --git a/benchmark/tune.json b/benchmark/tune.json new file mode 100644 index 000000000..8308299c5 --- /dev/null +++ b/benchmark/tune.json @@ -0,0 +1 @@ +[{"Julia":"1.7.1","BenchmarkTools":"1.0.0"},[["BenchmarkGroup",{"data":{"e_mul_xj":["BenchmarkGroup",{"data":{"spmm0":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmv":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmm2":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmv0":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmm1":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}]},"tags":[]}]},"tags":[]}]]] \ No newline at end of file From 9a61624e31b39889159aef904ebc701cebcf0566 Mon Sep 17 00:00:00 2001 From: Carlo Lucibello Date: Fri, 25 Feb 2022 08:24:49 +0100 Subject: [PATCH 2/6] more benchmarks --- benchmark/bench_gcnconv.jl | 9 ++++++ benchmark/bench_propagate.jl | 53 ++++++++++++++++++++++++++++++++ benchmark/benchmarks.jl | 58 ++++-------------------------------- benchmark/result.json | 2 +- benchmark/runbenchmarks.jl | 2 +- 5 files changed, 69 insertions(+), 55 deletions(-) create mode 100644 benchmark/bench_gcnconv.jl create mode 100644 benchmark/bench_propagate.jl diff --git a/benchmark/bench_gcnconv.jl b/benchmark/bench_gcnconv.jl new file mode 100644 index 000000000..2a0a251c8 --- /dev/null +++ b/benchmark/bench_gcnconv.jl @@ -0,0 +1,9 @@ +module BenchGCNConv +using GraphNeuralNetworks +using Random, Statistics, SparseArrays +using BenchmarkTools + +SUITE = BenchmarkGroup() + +end +BenchGCNConv.SUITE \ No newline at end of file diff --git a/benchmark/bench_propagate.jl b/benchmark/bench_propagate.jl new file mode 100644 index 000000000..eeb2c9aa6 --- /dev/null +++ b/benchmark/bench_propagate.jl @@ -0,0 +1,53 @@ +module BenchPropagate +using GraphNeuralNetworks +using Random, Statistics, SparseArrays +using BenchmarkTools + +SUITE = BenchmarkGroup() + +Random.seed!(17) +n = 1024 +A = sprand(n, n, 0.01) +v = rand(1, n) +X = rand(100, n) +w = reshape(A.nzval, 1, :) +e = rand(1, length(A.nzval)) + +g = GNNGraph(A, ndata=(; v, X), edata=(; e, w), graph_type=:coo) + +spmv(g) = propagate((xi, xj, e) -> e .* xj, g, +; xj=g.ndata.v, e=g.edata.w) +spmm1(g) = propagate((xi, xj, e) -> e .* xj , g, +; xj=g.ndata.X, e=g.edata.w) +spmm2(g) = propagate(e_mul_xj, g, +; xj=g.ndata.X, e=vec(g.edata.w)) + +@assert spmv(g) ≈ v * A +@assert spmm1(g) ≈ X * A +@assert spmm2(g) ≈ X * A + +let sub = SUITE["e_mul_xj"] = BenchmarkGroup() + sub["spmv0"] = @benchmarkable $v * $A + sub["spmv"] = @benchmarkable spmv($g) + sub["spmm0"] = @benchmarkable $X * $A + sub["spmm1"] = @benchmarkable spmm1($g) + sub["spmm2"] = @benchmarkable spmm2($g) +end + +spmm_copyxj_fused(g) = propagate(copy_xj, g, +; xj=g.ndata.X) +spmm_copyxj_unfused(g) = propagate((xi, xj, e) -> xj, g, +; xj=g.ndata.X) + + +let sub = SUITE["copy_xj"] = BenchmarkGroup() + Adj = map(x -> x > 0 ? 1 : 0, A) + @assert spmm_copyxj_unfused(g) ≈ X * Adj + @assert spmm_copyxj_fused(g) ≈ X * Adj + + sub["copyxj0"] = @benchmarkable $X * $Adj + sub["copyxj_fused"] = @benchmarkable spmm_copyxj_fused($g) + sub["copyxj_unfused"] = @benchmarkable spmm_copyxj_unfused($g) +end + +# @btime spmm_copyxj_fused(g) # 268.614 μs (22 allocations: 1.13 MiB) +# @btime spmm_copyxj_unfused(g) # 4.263 ms (52855 allocations: 12.23 MiB) +# @btime B * Adj # 196.135 μs (2 allocations: 800.05 KiB) + +end +BenchPropagate.SUITE \ No newline at end of file diff --git a/benchmark/benchmarks.jl b/benchmark/benchmarks.jl index 714847f5b..19fc9c666 100644 --- a/benchmark/benchmarks.jl +++ b/benchmark/benchmarks.jl @@ -1,57 +1,9 @@ -using SparseArrays -using GraphNeuralNetworks using BenchmarkTools -import Random: seed! -using LinearAlgebra SUITE = BenchmarkGroup() - -seed!(17) -n = 1024 -A = sprand(n, n, 0.01) -v = rand(1, n) -X = rand(100, n) -w = reshape(A.nzval, 1, :) -e = rand(1, length(A.nzval)) - -g = GNNGraph(A, ndata=(; v, X), edata=(; e, w), graph_type=:coo) - -spmv(g) = propagate((xi, xj, e) -> e .* xj, g, +; xj=g.ndata.v, e=g.edata.w) -spmm1(g) = propagate((xi, xj, e) -> e .* xj , g, +; xj=g.ndata.X, e=g.edata.w) -spmm2(g) = propagate(e_mul_xj, g, +; xj=g.ndata.X, e=vec(g.edata.w)) - -@assert isequal(spmv(g), v * A) -@assert isequal(spmm1(g), X * A) -@assert isequal(spmm2(g), X * A) - -let sub = SUITE["e_mul_xj"] = BenchmarkGroup() - sub["spmv0"] = @benchmarkable $v * $A - sub["spmv"] = @benchmarkable spmv($g) - sub["spmm0"] = @benchmarkable $X * $A - sub["spmm1"] = @benchmarkable spmm1($g) - sub["spmm2"] = @benchmarkable spmm2($g) +for file in readdir(@__DIR__) + if startswith(file, "bench_") && endswith(file, ".jl") + SUITE[file[length("bench_") + 1:end - length(".jl")]] = + include(file) + end end - -# function spmm_copyxj_fused(g) -# propagate( -# copy_xj, -# g, +; xj=g.ndata.B -# ) -# end - -# function spmm_copyxj_unfused(g) -# propagate( -# (xi, xj, e) -> xj, -# g, +; xj=g.ndata.B -# ) -# end - -# Adj = map(x -> x > 0 ? 1 : 0, A) -# @assert spmm_copyxj_unfused(g) ≈ B * Adj -# @assert spmm_copyxj_fused(g) ≈ B * Adj # bug fixed in #107 - -# @btime spmm_copyxj_fused(g) # 268.614 μs (22 allocations: 1.13 MiB) -# @btime spmm_copyxj_unfused(g) # 4.263 ms (52855 allocations: 12.23 MiB) -# @btime B * Adj # 196.135 μs (2 allocations: 800.05 KiB) - -# println() \ No newline at end of file diff --git a/benchmark/result.json b/benchmark/result.json index a3a80ae62..a3e1e28ac 100644 --- a/benchmark/result.json +++ b/benchmark/result.json @@ -1 +1 @@ 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Version 1.7.1\nCommit ac5cc99908 (2021-12-22 19:35 UTC)\nPlatform Info:\n OS: Linux (x86_64-pc-linux-gnu)\n \"Manjaro Linux\"\n uname: Linux 5.16.7-1-MANJARO #1 SMP PREEMPT Sun Feb 6 12:22:29 UTC 2022 x86_64 unknown\n CPU: Intel(R) Core(TM) i7-10710U CPU @ 1.10GHz: \n speed user nice sys idle irq\n #1-12 1600 MHz 36215 s 1955 s 7574 s 654296 s 5912 s\n \n Memory: 15.464141845703125 GB (3893.1796875 MB free)\n Uptime: 33856.87 sec\n Load Avg: 1.33 1.69 1.58\n WORD_SIZE: 64\n LIBM: libopenlibm\n LLVM: libLLVM-12.0.1 (ORCJIT, skylake)\n","commit":"dirty","julia_commit":"ac5cc99908d463582e66db3368b9b48fae1e2525","date":"2022-02-25T08:06:25.745"} \ No newline at end of file diff --git a/benchmark/runbenchmarks.jl b/benchmark/runbenchmarks.jl index 30a4458b8..3ee95a83d 100644 --- a/benchmark/runbenchmarks.jl +++ b/benchmark/runbenchmarks.jl @@ -3,7 +3,7 @@ using PkgBenchmark benchmarkpkg( dirname(@__DIR__), BenchmarkConfig( - id = "v0.3.13", + # id = "v0.3.13", env = Dict( "JULIA_NUM_THREADS" => "1", "OMP_NUM_THREADS" => "1", From 6f3883867ab7c570c1b9fb4128af07f24820b5af Mon Sep 17 00:00:00 2001 From: Carlo Lucibello Date: Fri, 25 Feb 2022 08:26:09 +0100 Subject: [PATCH 3/6] cleanup --- benchmark/perf.jl | 106 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 benchmark/perf.jl diff --git a/benchmark/perf.jl b/benchmark/perf.jl new file mode 100644 index 000000000..dfebc0de2 --- /dev/null +++ b/benchmark/perf.jl @@ -0,0 +1,106 @@ +using Flux, GraphNeuralNetworks, Graphs, BenchmarkTools, CUDA +using DataFrames, Statistics, JLD2, SparseArrays +CUDA.device!(2) +CUDA.allowscalar(false) + +BenchmarkTools.ratio(::Missing, x) = Inf +BenchmarkTools.ratio(x, ::Missing) = 0.0 +BenchmarkTools.ratio(::Missing, ::Missing) = missing + +function run_single_benchmark(N, c, D, CONV; gtype=:lg) + data = erdos_renyi(N, c / (N-1), seed=17) + X = randn(Float32, D, N) + + g = GNNGraph(data; ndata=X, graph_type=gtype) + g_gpu = g |> gpu + + m = CONV(D => D) + ps = Flux.params(m) + + m_gpu = m |> gpu + ps_gpu = Flux.params(m_gpu) + + + res = Dict() + + res["CPU_FWD"] = @benchmark $m($g) + res["CPU_GRAD"] = @benchmark gradient(() -> sum($m($g).ndata.x), $ps) + + try + res["GPU_FWD"] = @benchmark CUDA.@sync($m_gpu($g_gpu)) teardown=(GC.gc(); CUDA.reclaim()) + catch + res["GPU_FWD"] = missing + end + + try + res["GPU_GRAD"] = @benchmark CUDA.@sync(gradient(() -> sum($m_gpu($g_gpu).ndata.x), $ps_gpu)) teardown=(GC.gc(); CUDA.reclaim()) + catch + res["GPU_GRAD"] = missing + end + + return res +end + +""" + run_benchmarks(; + Ns = [10, 100, 1000, 10000], + c = 6, + D = 100, + layers = [GCNConv, GraphConv, GATConv] + ) + +Benchmark GNN layers on Erdos-Renyi ranomd graphs +with average degree `c`. Benchmarks are perfomed for each graph size in the list `Ns`. +`D` is the number of node features. +""" +function run_benchmarks(; + Ns = [10, 100, 1000, 10000], + c = 6, + D = 100, + layers = [GCNConv, GATConv], + gtypes = [:coo, :sparse, :dense], + ) + + df = DataFrame(N=Int[], c=Float64[], layer=String[], gtype=Symbol[], + time_cpu=Any[], time_gpu=Any[]) |> allowmissing + + for gtype in gtypes + for N in Ns + println("## GRAPH_TYPE = $gtype N = $N") + for CONV in layers + res = run_single_benchmark(N, c, D, CONV; gtype) + row = (;layer = "$CONV", + N = N, + c = c, + gtype = gtype, + time_cpu = ismissing(res["CPU"]) ? missing : median(res["CPU"]), + time_gpu = ismissing(res["GPU"]) ? missing : median(res["GPU"]), + ) + push!(df, row) + end + end + end + + df.gpu_to_cpu = ratio.(df.time_gpu, df.time_cpu) + sort!(df, [:layer, :N, :c, :gtype]) + return df +end + +# df = run_benchmarks() +# for g in groupby(df, :layer); println(g, "\n"); end + +# @save "perf/perf_master_20210803_carlo.jld2" dfmaster=df +## or +# @save "perf/perf_pr.jld2" dfpr=df + + +function compare(dfpr, dfmaster; on=[:N, :c, :gtype, :layer]) + df = outerjoin(dfpr, dfmaster; on=on, makeunique=true, renamecols = :_pr => :_master) + df.pr_to_master_cpu = ratio.(df.time_cpu_pr, df.time_cpu_master) + df.pr_to_master_gpu = ratio.(df.time_gpu_pr, df.time_gpu_master) + return df[:,[:N, :c, :gtype, :layer, :pr_to_master_cpu, :pr_to_master_gpu]] +end + +# @load "perf/perf_pr.jld2" dfpr +# @load "perf/perf_master.jld2" dfmaster +# compare(dfpr, dfmaster) From e654972358a77381d3d282ad77d66d1ec02162bf Mon Sep 17 00:00:00 2001 From: Carlo Lucibello Date: Fri, 25 Feb 2022 08:26:48 +0100 Subject: [PATCH 4/6] cleanup --- perf/Project.toml | 6 -- perf/bench_gnn.jl | 73 ---------------- perf/perf.jl | 106 ----------------------- perf/perf_master_20210830_carlodsba.jld2 | Bin 29970 -> 0 bytes 4 files changed, 185 deletions(-) delete mode 100644 perf/Project.toml delete mode 100644 perf/bench_gnn.jl delete mode 100644 perf/perf.jl delete mode 100644 perf/perf_master_20210830_carlodsba.jld2 diff --git a/perf/Project.toml b/perf/Project.toml deleted file mode 100644 index ddbb1be6e..000000000 --- a/perf/Project.toml +++ /dev/null @@ -1,6 +0,0 @@ -[deps] -BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694" -JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" -Graphs = "093fc24a-ae57-5d10-9952-331d41423f4d" diff --git a/perf/bench_gnn.jl b/perf/bench_gnn.jl deleted file mode 100644 index 0e1b35208..000000000 --- a/perf/bench_gnn.jl +++ /dev/null @@ -1,73 +0,0 @@ -using SparseArrays -using GraphNeuralNetworks -using BenchmarkTools -import Random: seed! -using LinearAlgebra - -n = 1024 -seed!(0) -A = sprand(n, n, 0.01) -b = rand(1, n) -B = rand(100, n) - -g = GNNGraph( - A, - ndata=(; b=b, B=B), - edata=(; A=reshape(A.nzval, 1, :)), - graph_type=:coo # changing to :sparse has little effect on performance -) - -function spmv(g) - propagate( - (xi, xj, e) -> e .* xj , # same as e_mul_xj - g, +; xj=g.ndata.b, e=g.edata.A - ) -end - -function spmm1(g) - propagate( - (xi, xj, e) -> e .* xj , # same as e_mul_xj - g, +; xj=g.ndata.B, e=g.edata.A - ) -end -function spmm2(g) - propagate( - e_mul_xj, - g, +; xj=g.ndata.B, e=vec(g.edata.A) - ) -end - -# @assert isequal(spmv(g), b * A) # true -# @btime spmv(g) # ~5 ms -# @btime b * A # ~32 us - -@assert isequal(spmm1(g), B * A) # true -@assert isequal(spmm2(g), B * A) # true -@btime spmm1(g) # ~9 ms -@btime spmm2(g) # ~9 ms -@btime B * A # ~400 us - - -function spmm_copyxj_fused(g) - propagate( - copy_xj, - g, +; xj=g.ndata.B - ) -end - -function spmm_copyxj_unfused(g) - propagate( - (xi, xj, e) -> xj, - g, +; xj=g.ndata.B - ) -end - -Adj = map(x -> x > 0 ? 1 : 0, A) -@assert spmm_copyxj_unfused(g) ≈ B * Adj -@assert spmm_copyxj_fused(g) ≈ B * Adj # bug fixed in #107 - -@btime spmm_copyxj_fused(g) # 268.614 μs (22 allocations: 1.13 MiB) -@btime spmm_copyxj_unfused(g) # 4.263 ms (52855 allocations: 12.23 MiB) -@btime B * Adj # 196.135 μs (2 allocations: 800.05 KiB) - -println() \ No newline at end of file diff --git a/perf/perf.jl b/perf/perf.jl deleted file mode 100644 index dfebc0de2..000000000 --- a/perf/perf.jl +++ /dev/null @@ -1,106 +0,0 @@ -using Flux, GraphNeuralNetworks, Graphs, BenchmarkTools, CUDA -using DataFrames, Statistics, JLD2, SparseArrays -CUDA.device!(2) -CUDA.allowscalar(false) - -BenchmarkTools.ratio(::Missing, x) = Inf -BenchmarkTools.ratio(x, ::Missing) = 0.0 -BenchmarkTools.ratio(::Missing, ::Missing) = missing - -function run_single_benchmark(N, c, D, CONV; gtype=:lg) - data = erdos_renyi(N, c / (N-1), seed=17) - X = randn(Float32, D, N) - - g = GNNGraph(data; ndata=X, graph_type=gtype) - g_gpu = g |> gpu - - m = CONV(D => D) - ps = Flux.params(m) - - m_gpu = m |> gpu - ps_gpu = Flux.params(m_gpu) - - - res = Dict() - - res["CPU_FWD"] = @benchmark $m($g) - res["CPU_GRAD"] = @benchmark gradient(() -> sum($m($g).ndata.x), $ps) - - try - res["GPU_FWD"] = @benchmark CUDA.@sync($m_gpu($g_gpu)) teardown=(GC.gc(); CUDA.reclaim()) - catch - res["GPU_FWD"] = missing - end - - try - res["GPU_GRAD"] = @benchmark CUDA.@sync(gradient(() -> sum($m_gpu($g_gpu).ndata.x), $ps_gpu)) teardown=(GC.gc(); CUDA.reclaim()) - catch - res["GPU_GRAD"] = missing - end - - return res -end - -""" - run_benchmarks(; - Ns = [10, 100, 1000, 10000], - c = 6, - D = 100, - layers = [GCNConv, GraphConv, GATConv] - ) - -Benchmark GNN layers on Erdos-Renyi ranomd graphs -with average degree `c`. Benchmarks are perfomed for each graph size in the list `Ns`. -`D` is the number of node features. -""" -function run_benchmarks(; - Ns = [10, 100, 1000, 10000], - c = 6, - D = 100, - layers = [GCNConv, GATConv], - gtypes = [:coo, :sparse, :dense], - ) - - df = DataFrame(N=Int[], c=Float64[], layer=String[], gtype=Symbol[], - time_cpu=Any[], time_gpu=Any[]) |> allowmissing - - for gtype in gtypes - for N in Ns - println("## GRAPH_TYPE = $gtype N = $N") - for CONV in layers - res = run_single_benchmark(N, c, D, CONV; gtype) - row = (;layer = "$CONV", - N = N, - c = c, - gtype = gtype, - time_cpu = ismissing(res["CPU"]) ? missing : median(res["CPU"]), - time_gpu = ismissing(res["GPU"]) ? missing : median(res["GPU"]), - ) - push!(df, row) - end - end - end - - df.gpu_to_cpu = ratio.(df.time_gpu, df.time_cpu) - sort!(df, [:layer, :N, :c, :gtype]) - return df -end - -# df = run_benchmarks() -# for g in groupby(df, :layer); println(g, "\n"); end - -# @save "perf/perf_master_20210803_carlo.jld2" dfmaster=df -## or -# @save "perf/perf_pr.jld2" dfpr=df - - -function compare(dfpr, dfmaster; on=[:N, :c, :gtype, :layer]) - df = outerjoin(dfpr, dfmaster; on=on, makeunique=true, renamecols = :_pr => :_master) - df.pr_to_master_cpu = ratio.(df.time_cpu_pr, df.time_cpu_master) - df.pr_to_master_gpu = ratio.(df.time_gpu_pr, df.time_gpu_master) - return df[:,[:N, :c, :gtype, :layer, :pr_to_master_cpu, :pr_to_master_gpu]] -end - -# @load "perf/perf_pr.jld2" dfpr -# @load "perf/perf_master.jld2" dfmaster -# compare(dfpr, dfmaster) diff --git a/perf/perf_master_20210830_carlodsba.jld2 b/perf/perf_master_20210830_carlodsba.jld2 deleted file mode 100644 index 6669c889e724623b32a8db6c19a1066c9e11a6af..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 29970 zcmeHQ30zdw_rK$y4hRa0fJ-{AEFuCTX{0kCf(t4lYT|+ejDil#Ff(ANB$X+qsJWE6 zlnd1_Euu8H%+O46%eAtMUzq*0Qj^?LbNS!<&be=far`yZpZQIFxF4PK?z!*X_dUzq 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benchmark/Manifest.toml | 721 +++++++++++++ docs/Manifest.toml | 608 +++++++++++ examples/Manifest.toml | 1737 +++++++++++++++++++++++++++++++ 5 files changed, 3092 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/benchmark.yml create mode 100644 benchmark/Manifest.toml create mode 100644 docs/Manifest.toml create mode 100644 examples/Manifest.toml diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml new file mode 100644 index 000000000..27a66d7d1 --- /dev/null +++ b/.github/workflows/benchmark.yml @@ -0,0 +1,25 @@ +name: Run benchmarks + +on: + pull_request: + types: [labeled, opened, synchronize, reopened] + +# Only trigger the benchmark job when you add `run benchmark` label to the PR +jobs: + Benchmark: + runs-on: ubuntu-latest + if: contains(github.event.pull_request.labels.*.name, 'run benchmark') + steps: + - uses: actions/checkout@v2 + - uses: julia-actions/setup-julia@latest + with: + version: 1 + - uses: julia-actions/julia-buildpkg@latest + - name: Install dependencies + run: julia -e 'using Pkg; pkg"add PkgBenchmark BenchmarkCI@0.1"' + - name: Run benchmarks + run: julia -e 'using BenchmarkCI; BenchmarkCI.judge()' + - name: Post results + run: julia -e 'using BenchmarkCI; BenchmarkCI.postjudge()' + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.gitignore b/.gitignore index 5be50c11d..ff3a193e7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,6 @@ *.jl.*.cov *.jl.cov *.jl.mem -Manifest.toml +/Manifest.toml /docs/build/ .vscode diff --git a/benchmark/Manifest.toml b/benchmark/Manifest.toml new file mode 100644 index 000000000..d0393c8ec --- /dev/null +++ b/benchmark/Manifest.toml @@ -0,0 +1,721 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.7.1" +manifest_format = "2.0" + +[[deps.AbstractFFTs]] +deps = ["ChainRulesCore", "LinearAlgebra"] +git-tree-sha1 = "6f1d9bc1c08f9f4a8fa92e3ea3cb50153a1b40d4" +uuid = 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b/docs/Manifest.toml @@ -0,0 +1,608 @@ +# This file is machine-generated - editing it directly is not advised + +[[ANSIColoredPrinters]] +git-tree-sha1 = "574baf8110975760d391c710b6341da1afa48d8c" +uuid = "a4c015fc-c6ff-483c-b24f-f7ea428134e9" +version = "0.0.1" + +[[AbstractFFTs]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0" +uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" +version = "1.0.1" + +[[AbstractTrees]] +git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5" +uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" +version = "0.3.4" + +[[Adapt]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7" +uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" +version = "3.3.1" + +[[ArgTools]] +uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" + +[[ArnoldiMethod]] +deps = ["LinearAlgebra", "Random", "StaticArrays"] +git-tree-sha1 = "62e51b39331de8911e4a7ff6f5aaf38a5f4cc0ae" +uuid = "ec485272-7323-5ecc-a04f-4719b315124d" +version = 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+ +[[WoodburyMatrices]] +deps = ["LinearAlgebra", "SparseArrays"] +git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3" +uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6" +version = "0.5.5" + +[[ZipFile]] +deps = ["Libdl", "Printf", "Zlib_jll"] +git-tree-sha1 = "3593e69e469d2111389a9bd06bac1f3d730ac6de" +uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" +version = "0.9.4" + +[[Zlib_jll]] +deps = ["Libdl"] +uuid = "83775a58-1f1d-513f-b197-d71354ab007a" + +[[Zygote]] +deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"] +git-tree-sha1 = "88a4d79f4e389456d5a90d79d53d1738860ef0a5" +uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" +version = "0.6.34" + +[[ZygoteRules]] +deps = ["MacroTools"] +git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" +uuid = "700de1a5-db45-46bc-99cf-38207098b444" +version = "0.2.2" + +[[libblastrampoline_jll]] +deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" + +[[nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" + +[[p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" From 660dfcfc40bfcd07dedd1806ce86e05df555335f Mon Sep 17 00:00:00 2001 From: Carlo Lucibello Date: Fri, 25 Feb 2022 18:29:48 +0100 Subject: [PATCH 6/6] more benchmarks --- benchmark/Manifest.toml | 37 +++--------------- benchmark/Project.toml | 3 +- benchmark/bench_gcnconv.jl | 75 +++++++++++++++++++++++++++++++++++- benchmark/bench_propagate.jl | 23 ++++------- benchmark/benchmark.md | 57 +++++++++++++++++++++++++++ benchmark/result.json | 1 - benchmark/runbenchmarks.jl | 24 ++++++------ benchmark/tune.json | 2 +- 8 files changed, 160 insertions(+), 62 deletions(-) create mode 100644 benchmark/benchmark.md delete mode 100644 benchmark/result.json diff --git a/benchmark/Manifest.toml b/benchmark/Manifest.toml index d0393c8ec..a73a69b30 100644 --- a/benchmark/Manifest.toml +++ b/benchmark/Manifest.toml @@ -25,9 +25,9 @@ uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" [[deps.ArnoldiMethod]] deps = ["LinearAlgebra", "Random", "StaticArrays"] -git-tree-sha1 = "f87e559f87a45bece9c9ed97458d3afe98b1ebb9" +git-tree-sha1 = "62e51b39331de8911e4a7ff6f5aaf38a5f4cc0ae" uuid = "ec485272-7323-5ecc-a04f-4719b315124d" -version = "0.1.0" +version = "0.2.0" [[deps.ArrayInterface]] deps = ["Compat", "IfElse", "LinearAlgebra", "Requires", "SparseArrays", "Static"] @@ -246,36 +246,11 @@ uuid = "61eb1bfa-7361-4325-ad38-22787b887f55" version = "0.13.14" [[deps.GraphNeuralNetworks]] -git-tree-sha1 = "4d6307b7b3ff04fccb46a8d72e4f2794d3790f4a" +deps = ["Adapt", "CUDA", "ChainRulesCore", "DataStructures", "Flux", "Functors", "Graphs", "KrylovKit", "LearnBase", "LinearAlgebra", "MacroTools", "NNlib", "NNlibCUDA", "NearestNeighbors", "Random", "Reexport", "SparseArrays", "Statistics", "StatsBase"] +path = ".." uuid = "cffab07f-9bc2-4db1-8861-388f63bf7694" version = "0.3.13" - [deps.GraphNeuralNetworks.deps] - Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" - CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" - ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" - DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" - Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" - Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196" - Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" - KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77" - LearnBase = "7f8f8fb0-2700-5f03-b4bd-41f8cfc144b6" - LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" - MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" - NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" - NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d" - NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce" - Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" - Reexport = "189a3867-3050-52da-a836-e630ba90ab69" - SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" - Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" - StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" - -[[deps.Graphs]] -deps = ["ArnoldiMethod", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"] -git-tree-sha1 = "432428df5f360964040ed60418dd5601ecd240b6" -uuid = "093fc24a-ae57-5d10-9952-331d41423f4d" -version = "1.3.5" [[deps.Graphs]] deps = ["ArnoldiMethod", "Compat", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"] git-tree-sha1 = "57c021de207e234108a6f1454003120a1bf350c4" @@ -605,9 +580,9 @@ uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" [[deps.SpecialFunctions]] deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] -git-tree-sha1 = "85e5b185ed647b8ee89aa25a7788a2b43aa8a74f" +git-tree-sha1 = "5ba658aeecaaf96923dce0da9e703bd1fe7666f9" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "2.1.3" +version = "2.1.4" [[deps.Static]] deps = ["IfElse"] diff --git a/benchmark/Project.toml b/benchmark/Project.toml index 0a7fe3966..9e49aa3eb 100644 --- a/benchmark/Project.toml +++ b/benchmark/Project.toml @@ -1,7 +1,8 @@ [deps] BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694" -Graphs = "093fc24a-ae57-5d10-9952-331d41423f4d" +Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" PkgBenchmark = "32113eaa-f34f-5b0d-bd6c-c81e245fc73d" diff --git a/benchmark/bench_gcnconv.jl b/benchmark/bench_gcnconv.jl index 2a0a251c8..8b12e2936 100644 --- a/benchmark/bench_gcnconv.jl +++ b/benchmark/bench_gcnconv.jl @@ -2,8 +2,81 @@ module BenchGCNConv using GraphNeuralNetworks using Random, Statistics, SparseArrays using BenchmarkTools +using Flux, Graphs -SUITE = BenchmarkGroup() +function get_single_benchmark(sub, N, c, D, CONV; gtype=:lg) + data = erdos_renyi(N, c / (N-1), seed=17) + X = randn(Float32, D, N) + + g = GNNGraph(data; ndata=X, graph_type=gtype) + g_gpu = g |> gpu + + m = CONV(D => D) + ps = Flux.params(m) + + m_gpu = m |> gpu + ps_gpu = Flux.params(m_gpu) + + + sub["CPU_FWD"] = @benchmarkable $m($g) + sub["CPU_GRAD"] = @benchmarkable gradient(() -> sum($m($g).ndata.x), $ps) + + # try + # sub["GPU_FWD"] = @benchmark CUDA.@sync($m_gpu($g_gpu)) teardown=(GC.gc(); CUDA.reclaim()) + # catch + # sub["GPU_FWD"] = missing + # end + + # try + # sub["GPU_GRAD"] = @benchmark CUDA.@sync(gradient(() -> sum($m_gpu($g_gpu).ndata.x), $ps_gpu)) teardown=(GC.gc(); CUDA.reclaim()) + # catch + # # sub["GPU_GRAD"] = missing + # end + + return sub +end + + + +""" + run_benchmarks(; + Ns = [10, 100, 1000, 10000], + c = 6, + D = 100, + layers = [GCNConv, GraphConv, GATConv] + ) + +Benchmark GNN layers on Erdos-Renyi ranomd graphs +with average degree `c`. Benchmarks are perfomed for each graph size in the list `Ns`. +`D` is the number of node features. +""" +function get_benchmarks(; + # Ns = [10, 100, 1000, 10000], + Ns = [10, 100], + c = 6, + D = 100, + # layers = [GCNConv, GATConv], + # gtypes = [:coo, :sparse, :dense], + layers = [GCNConv,], + gtypes = [:coo,], + ) + + SUITE = BenchmarkGroup() + + for gtype in gtypes + for N in Ns + for CONV in layers + sub = SUITE["$(gtype)_$(N)_$(CONV)"] = BenchmarkGroup() + get_single_benchmark(sub, N, c, D, CONV; gtype) + end + end + end + return SUITE end + +SUITE = get_benchmarks() + +end + BenchGCNConv.SUITE \ No newline at end of file diff --git a/benchmark/bench_propagate.jl b/benchmark/bench_propagate.jl index eeb2c9aa6..1ea5e7927 100644 --- a/benchmark/bench_propagate.jl +++ b/benchmark/bench_propagate.jl @@ -8,27 +8,22 @@ SUITE = BenchmarkGroup() Random.seed!(17) n = 1024 A = sprand(n, n, 0.01) -v = rand(1, n) X = rand(100, n) w = reshape(A.nzval, 1, :) e = rand(1, length(A.nzval)) -g = GNNGraph(A, ndata=(; v, X), edata=(; e, w), graph_type=:coo) +g = GNNGraph(A, ndata=(; X), edata=(; e, w), graph_type=:coo) -spmv(g) = propagate((xi, xj, e) -> e .* xj, g, +; xj=g.ndata.v, e=g.edata.w) spmm1(g) = propagate((xi, xj, e) -> e .* xj , g, +; xj=g.ndata.X, e=g.edata.w) spmm2(g) = propagate(e_mul_xj, g, +; xj=g.ndata.X, e=vec(g.edata.w)) -@assert spmv(g) ≈ v * A @assert spmm1(g) ≈ X * A @assert spmm2(g) ≈ X * A let sub = SUITE["e_mul_xj"] = BenchmarkGroup() - sub["spmv0"] = @benchmarkable $v * $A - sub["spmv"] = @benchmarkable spmv($g) - sub["spmm0"] = @benchmarkable $X * $A - sub["spmm1"] = @benchmarkable spmm1($g) - sub["spmm2"] = @benchmarkable spmm2($g) + sub["_baseline"] = @benchmarkable $X * $A + sub["unfused"] = @benchmarkable spmm1($g) + sub["fused"] = @benchmarkable spmm2($g) end spmm_copyxj_fused(g) = propagate(copy_xj, g, +; xj=g.ndata.X) @@ -40,14 +35,10 @@ let sub = SUITE["copy_xj"] = BenchmarkGroup() @assert spmm_copyxj_unfused(g) ≈ X * Adj @assert spmm_copyxj_fused(g) ≈ X * Adj - sub["copyxj0"] = @benchmarkable $X * $Adj - sub["copyxj_fused"] = @benchmarkable spmm_copyxj_fused($g) - sub["copyxj_unfused"] = @benchmarkable spmm_copyxj_unfused($g) + sub["_baseline"] = @benchmarkable $X * $Adj + sub["fused"] = @benchmarkable spmm_copyxj_fused($g) + sub["unfused"] = @benchmarkable spmm_copyxj_unfused($g) end -# @btime spmm_copyxj_fused(g) # 268.614 μs (22 allocations: 1.13 MiB) -# @btime spmm_copyxj_unfused(g) # 4.263 ms (52855 allocations: 12.23 MiB) -# @btime B * Adj # 196.135 μs (2 allocations: 800.05 KiB) - end BenchPropagate.SUITE \ No newline at end of file diff --git a/benchmark/benchmark.md b/benchmark/benchmark.md new file mode 100644 index 000000000..e57a37b5b --- /dev/null +++ b/benchmark/benchmark.md @@ -0,0 +1,57 @@ +# Benchmark Report for */home/carlo/.julia/dev/GraphNeuralNetworks* + +## Job Properties +* Time of benchmark: 25 Feb 2022 - 16:17 +* Package commit: dirty +* Julia commit: ac5cc9 +* Julia command flags: None +* Environment variables: `OMP_NUM_THREADS => 1` `JULIA_NUM_THREADS => 1` + +## Results +Below is a table of this job's results, obtained by running the benchmarks. +The values listed in the `ID` column have the structure `[parent_group, child_group, ..., key]`, and can be used to +index into the BaseBenchmarks suite to retrieve the corresponding benchmarks. +The percentages accompanying time and memory values in the below table are noise tolerances. The "true" +time/memory value for a given benchmark is expected to fall within this percentage of the reported value. +An empty cell means that the value was zero. + +| ID | time | GC time | memory | allocations | +|------------------------------------------------------------------|----------------:|--------:|----------------:|------------:| +| `["gcnconv", "coo_100_GraphNeuralNetworks.GCNConv", "CPU_FWD"]` | 86.176 μs (5%) | | 249.62 KiB (1%) | 58 | +| `["gcnconv", "coo_100_GraphNeuralNetworks.GCNConv", "CPU_GRAD"]` | 2.688 ms (5%) | | 757.31 KiB (1%) | 1887 | +| `["gcnconv", "coo_10_GraphNeuralNetworks.GCNConv", "CPU_FWD"]` | 20.919 μs (5%) | | 27.80 KiB (1%) | 51 | +| `["gcnconv", "coo_10_GraphNeuralNetworks.GCNConv", "CPU_GRAD"]` | 910.193 μs (5%) | | 219.61 KiB (1%) | 1869 | +| `["propagate", "copy_xj", "_baseline"]` | 408.494 μs (5%) | | 800.05 KiB (1%) | 2 | +| `["propagate", "copy_xj", "fused"]` | 561.269 μs (5%) | | 1.21 MiB (1%) | 31 | +| `["propagate", "copy_xj", "unfused"]` | 2.065 ms (5%) | | 8.95 MiB (1%) | 10 | +| `["propagate", "e_mul_xj", "_baseline"]` | 373.447 μs (5%) | | 800.05 KiB (1%) | 2 | +| `["propagate", "e_mul_xj", "fused"]` | 552.230 μs (5%) | | 1.13 MiB (1%) | 32 | +| `["propagate", "e_mul_xj", "unfused"]` | 3.596 ms (5%) | | 17.11 MiB (1%) | 12 | + +## Benchmark Group List +Here's a list of all the benchmark groups executed by this job: + +- `["gcnconv", "coo_100_GraphNeuralNetworks.GCNConv"]` +- `["gcnconv", "coo_10_GraphNeuralNetworks.GCNConv"]` +- `["propagate", "copy_xj"]` +- `["propagate", "e_mul_xj"]` + +## Julia versioninfo +``` +Julia Version 1.7.1 +Commit ac5cc99908 (2021-12-22 19:35 UTC) +Platform Info: + OS: Linux (x86_64-pc-linux-gnu) + "Manjaro Linux" + uname: Linux 5.16.7-1-MANJARO #1 SMP PREEMPT Sun Feb 6 12:22:29 UTC 2022 x86_64 unknown + CPU: Intel(R) Core(TM) i7-10710U CPU @ 1.10GHz: + speed user nice sys idle irq + #1-12 1185 MHz 484978 s 9416 s 116850 s 2788141 s 43648 s + + Memory: 15.464141845703125 GB (286.4765625 MB free) + Uptime: 63306.98 sec + Load Avg: 3.98 3.07 3.06 + WORD_SIZE: 64 + LIBM: libopenlibm + LLVM: libLLVM-12.0.1 (ORCJIT, skylake) +``` \ No newline at end of file diff --git a/benchmark/result.json b/benchmark/result.json deleted file mode 100644 index a3e1e28ac..000000000 --- a/benchmark/result.json +++ /dev/null @@ -1 +0,0 @@ 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Version 1.7.1\nCommit ac5cc99908 (2021-12-22 19:35 UTC)\nPlatform Info:\n OS: Linux (x86_64-pc-linux-gnu)\n \"Manjaro Linux\"\n uname: Linux 5.16.7-1-MANJARO #1 SMP PREEMPT Sun Feb 6 12:22:29 UTC 2022 x86_64 unknown\n CPU: Intel(R) Core(TM) i7-10710U CPU @ 1.10GHz: \n speed user nice sys idle irq\n #1-12 1600 MHz 36215 s 1955 s 7574 s 654296 s 5912 s\n \n Memory: 15.464141845703125 GB (3893.1796875 MB free)\n Uptime: 33856.87 sec\n Load Avg: 1.33 1.69 1.58\n WORD_SIZE: 64\n LIBM: libopenlibm\n LLVM: libLLVM-12.0.1 (ORCJIT, skylake)\n","commit":"dirty","julia_commit":"ac5cc99908d463582e66db3368b9b48fae1e2525","date":"2022-02-25T08:06:25.745"} \ No newline at end of file diff --git a/benchmark/runbenchmarks.jl b/benchmark/runbenchmarks.jl index 3ee95a83d..78dd89cd0 100644 --- a/benchmark/runbenchmarks.jl +++ b/benchmark/runbenchmarks.jl @@ -1,13 +1,15 @@ using PkgBenchmark -benchmarkpkg( - dirname(@__DIR__), - BenchmarkConfig( - # id = "v0.3.13", - env = Dict( - "JULIA_NUM_THREADS" => "1", - "OMP_NUM_THREADS" => "1", - ), - ), - resultfile = joinpath(@__DIR__, "result.json"), -) +results = benchmarkpkg( + dirname(@__DIR__), + BenchmarkConfig( + # id = "v0.3.13", + env = Dict( + "JULIA_NUM_THREADS" => "1", + "OMP_NUM_THREADS" => "1", + ), + ), + # resultfile = joinpath(@__DIR__, "result.json"), + ) + +export_markdown("benchmark.md", results) diff --git a/benchmark/tune.json b/benchmark/tune.json index 8308299c5..30740b995 100644 --- a/benchmark/tune.json +++ b/benchmark/tune.json @@ -1 +1 @@ -[{"Julia":"1.7.1","BenchmarkTools":"1.0.0"},[["BenchmarkGroup",{"data":{"e_mul_xj":["BenchmarkGroup",{"data":{"spmm0":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmv":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmm2":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmv0":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"spmm1":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}]},"tags":[]}]},"tags":[]}]]] \ No newline at end of file +[{"Julia":"1.7.1","BenchmarkTools":"1.0.0"},[["BenchmarkGroup",{"data":{"propagate":["BenchmarkGroup",{"data":{"e_mul_xj":["BenchmarkGroup",{"data":{"_baseline":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"fused":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"unfused":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}]},"tags":[]}],"copy_xj":["BenchmarkGroup",{"data":{"_baseline":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"fused":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"unfused":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}]},"tags":[]}]},"tags":[]}],"gcnconv":["BenchmarkGroup",{"data":{"coo_100_GraphNeuralNetworks.GCNConv":["BenchmarkGroup",{"data":{"CPU_FWD":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"CPU_GRAD":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}]},"tags":[]}],"coo_10_GraphNeuralNetworks.GCNConv":["BenchmarkGroup",{"data":{"CPU_FWD":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}],"CPU_GRAD":["Parameters",{"gctrial":true,"time_tolerance":0.05,"samples":10000,"evals":1,"gcsample":false,"seconds":5.0,"overhead":0.0,"memory_tolerance":0.01}]},"tags":[]}]},"tags":[]}]},"tags":[]}]]] \ No newline at end of file