add benchmark

.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 [email protected]"'
+      - 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 }}

.gitignore

@@ -24,4 +24,12 @@ docs/site/
 Manifest.toml
 
 # working repositories
-dvpt/**
+dvpt/**
+
+# Related to BenchmarkCI
+/.benchmarkci
+/benchmark/*.json
+
+# Related to PkgBenchmark
+benchmark/*.json
+benchmark/judgement.md

benchmark/Project.toml

@@ -0,0 +1,12 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+Krylov = "ba0b0d4f-ebba-5204-a429-3ac8c609bfb7"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
+PkgBenchmark = "32113eaa-f34f-5b0d-bd6c-c81e245fc73d"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+
+[compat]
+Krylov = "0.9.0"
+LinearOperators = "2.4.0"
+PkgBenchmark = "0.2"

benchmark/benchmarks.jl

@@ -0,0 +1,63 @@
+using BenchmarkTools
+using Kylov, LinearAlgebra
+using PartitionedVectors
+
+const SUITE = BenchmarkGroup()
+
+N = 5
+n = 8
+element_variables = [[1, 2, 3, 4], [3, 4, 5, 6], [5, 6, 7], [5, 6, 8], Int[]]
+
+epv = PS.create_epv(element_variables; type = Float32, n)
+pv_x = PartitionedVector(element_variables; T = Float32, n)
+
+epm = PS.epm_from_epv(epv)
+PS.update!(epm, epv, Float32(2.0) * epv; verbose = false)
+lo_epm = LinearOperators.LinearOperator(epm)
+
+solver = Krylov.CgSolver(pv_x)
+
+pv_gradient = PartitionedVector(element_variables; T = Float32, n)
+for i = 1:N
+  nie = pv_gradient[i].nie
+  pv_gradient[i] = rand(Float32, nie)
+end
+
+res = similar(pv_x)
+
+Krylov.solve!(solver, lo_epm, -pv_gradient)
+SUITE["Krylov small PartitionedVectors"] = BenchmarkGroup()
+SUITE["Krylov small PartitionedVectors"] = @benchmarkable Krylov.solve!(solver, lo_epm, -pv_gradient)
+
+SUITE["small broadcast"] = BenchmarkGroup()
+SUITE["small broadcast"] = @benchmarkable res .= pv_gradient .+ 3 .* pv_gradient
+
+
+N = 1500
+n = 20000
+nie = 15
+element_variables = map((i -> sample(1:n, nie, replace = false)), 1:N)
+
+epv = PS.create_epv(element_variables; type = Float32, n)
+pv_x = PartitionedVector(element_variables; T = Float32, n)
+
+epm = PS.epm_from_epv(epv)
+PS.update!(epm, epv, Float32(2.0) * epv; verbose = false)
+lo_epm = LinearOperators.LinearOperator(epm)
+
+solver = Krylov.CgSolver(pv_x)
+
+pv_gradient = PartitionedVector(element_variables; T = Float32, n)
+for i = 1:N
+  nie = pv_gradient[i].nie
+  pv_gradient[i] = rand(Float32, nie)
+end
+
+res = similar(pv_x)
+
+Krylov.solve!(solver, lo_epm, -pv_gradient)
+SUITE["Krylov big PartitionedVectors"] = BenchmarkGroup()
+SUITE["Krylov big PartitionedVectors"] = @benchmarkable Krylov.solve!(solver, lo_epm, -pv_gradient)
+
+SUITE["big broadcast"] = BenchmarkGroup()
+SUITE["big broadcast"] = @benchmarkable res .= pv_gradient .+ 3 .* pv_gradient

benchmark/run_benchmark.jl

@@ -0,0 +1,7 @@
+using PkgBenchmark
+
+commit = benchmarkpkg("Partitionedvectors")  #dernier commit sur la branche sur laquelle on se trouve
+master = benchmarkpkg("Partitionedvectors", "master") # branche master
+judgement = judge(commit, master)
+
+export_markdown("benchmark/judgement.md", judgement)