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Add benchmarks (#18)
* Add benchmarks * Add benchmarks to CI tests * Add benchmarks to tests * Add test dependencies * Fix typo * Add `PrecomputedNeighborhoodSearch` to benchmark plot * Fix typo * Migrate to v0.3 * Reformat * Fix benchmarks --------- Co-authored-by: Sven Berger <[email protected]>
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‎.github/workflows/ci.yml‎

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- uses: julia-actions/cache@v2
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- name: Build package
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uses: julia-actions/julia-buildpkg@v1
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- name: Run tests
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- name: Run unit tests
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uses: julia-actions/julia-runtest@v1
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with:
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annotate: true
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# Only run coverage in one Job (Ubuntu and latest Julia version)
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coverage: ${{ matrix.os == 'ubuntu-latest' && matrix.version == '1' }}
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env:
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POINTNEIGHBORS_TEST: unit
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- name: Process coverage results
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# Only run coverage in one Job (Ubuntu and latest Julia version)
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if: matrix.os == 'ubuntu-latest' && matrix.version == '1'
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flags: unit
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env:
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CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
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- name: Run benchmark tests
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uses: julia-actions/julia-runtest@v1
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with:
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annotate: true
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coverage: false
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env:
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POINTNEIGHBORS_TEST: benchmarks
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‎benchmarks/benchmarks.jl‎

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include("count_neighbors.jl")
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include("n_body.jl")
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include("plot.jl")

‎benchmarks/count_neighbors.jl‎

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using PointNeighbors
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using BenchmarkTools
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"""
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benchmark_count_neighbors(neighborhood_search, coordinates; parallel = true)
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A very cheap and simple neighborhood search benchmark, only counting the neighbors of each
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point. For each point-neighbor pair, only an array entry is incremented.
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Due to the minimal computational cost, differences between neighborhood search
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implementations are highlighted. On the other hand, this is the least realistic benchmark.
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For a computationally heavier benchmark, see [`benchmark_n_body`](@ref).
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"""
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function benchmark_count_neighbors(neighborhood_search, coordinates; parallel = true)
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n_neighbors = zeros(Int, size(coordinates, 2))
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function count_neighbors!(n_neighbors, coordinates, neighborhood_search, parallel)
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n_neighbors .= 0
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foreach_point_neighbor(coordinates, coordinates, neighborhood_search,
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parallel = parallel) do i, _, _, _
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n_neighbors[i] += 1
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end
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end
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return @belapsed $count_neighbors!($n_neighbors, $coordinates,
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$neighborhood_search, $parallel)
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end

‎benchmarks/n_body.jl‎

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using PointNeighbors
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using BenchmarkTools
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"""
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benchmark_n_body(neighborhood_search, coordinates; parallel = true)
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A simple neighborhood search benchmark, computing the right-hand side of an n-body
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simulation with a cutoff (corresponding to the search radius of `neighborhood_search`).
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This is a more realistic benchmark for particle-based simulations than
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[`benchmark_count_neighbors`](@ref).
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However, due to the higher computational cost, differences between neighborhood search
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implementations are less pronounced.
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"""
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function benchmark_n_body(neighborhood_search, coordinates; parallel = true)
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mass = 1e10 * (rand(size(coordinates, 2)) .+ 1)
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G = 6.6743e-11
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dv = similar(coordinates)
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function compute_acceleration!(dv, coordinates, mass, G, neighborhood_search, parallel)
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dv .= 0.0
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foreach_point_neighbor(coordinates, coordinates, neighborhood_search,
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parallel = parallel) do i, j, pos_diff, distance
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# Only consider particles with a distance > 0
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distance < sqrt(eps()) && return
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dv_ = -G * mass[j] * pos_diff / distance^3
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for dim in axes(dv, 1)
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@inbounds dv[dim, i] += dv_[dim]
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end
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end
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return dv
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end
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return @belapsed $compute_acceleration!($dv, $coordinates, $mass, $G,
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$neighborhood_search, $parallel)
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end

‎benchmarks/plot.jl‎

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using Plots
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using BenchmarkTools
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# Generate a rectangular point cloud
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include("../test/point_cloud.jl")
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"""
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plot_benchmarks(benchmark, n_points_per_dimension, iterations;
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seed = 1, perturbation_factor_position = 1.0,
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parallel = true, title = "")
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Run a benchmark for with several neighborhood searches multiple times for increasing numbers
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of points and plot the results.
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# Arguments
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- `benchmark`: The benchmark function. See [`benchmark_count_neighbors`](@ref)
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and [`benchmark_n_body`](@ref).
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- `n_points_per_dimension`: Initial resolution as tuple. The product is the initial number
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of points. For example, use `(100, 100)` for a 2D benchmark or
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`(10, 10, 10)` for a 3D benchmark.
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- `iterations`: Number of refinement iterations
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# Keywords
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- `parallel = true`: Loop over all points in parallel
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- `title = ""`: Title of the plot
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- `seed = 1`: Seed to perturb the point positions. Different seeds yield
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slightly different point positions.
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- `perturbation_factor_position = 1.0`: Perturb point positions by this factor. A factor of
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`1.0` corresponds to points being moved by
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a maximum distance of `0.5` along each axis.
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"""
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function plot_benchmarks(benchmark, n_points_per_dimension, iterations;
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parallel = true, title = "",
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seed = 1, perturbation_factor_position = 1.0)
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neighborhood_searches_names = ["TrivialNeighborhoodSearch";;
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"GridNeighborhoodSearch";;
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"PrecomputedNeighborhoodSearch"]
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# Multiply number of points in each iteration (roughly) by this factor
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scaling_factor = 4
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per_dimension_factor = scaling_factor^(1 / length(n_points_per_dimension))
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sizes = [round.(Int, n_points_per_dimension .* per_dimension_factor^(iter - 1))
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for iter in 1:iterations]
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n_particles_vec = prod.(sizes)
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times = zeros(iterations, length(neighborhood_searches_names))
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for iter in 1:iterations
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coordinates = point_cloud(sizes[iter], seed = seed,
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perturbation_factor_position = perturbation_factor_position)
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search_radius = 3.0
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NDIMS = size(coordinates, 1)
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n_particles = size(coordinates, 2)
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neighborhood_searches = [
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TrivialNeighborhoodSearch{NDIMS}(; search_radius, eachpoint = 1:n_particles),
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GridNeighborhoodSearch{NDIMS}(; search_radius, n_points = n_particles),
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PrecomputedNeighborhoodSearch{NDIMS}(; search_radius, n_points = n_particles),
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]
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for i in eachindex(neighborhood_searches)
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neighborhood_search = neighborhood_searches[i]
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initialize!(neighborhood_search, coordinates, coordinates)
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time = benchmark(neighborhood_search, coordinates, parallel = parallel)
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times[iter, i] = time
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time_string = BenchmarkTools.prettytime(time * 1e9)
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println("$(neighborhood_searches_names[i])")
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println("with $(join(sizes[iter], "x")) = $(prod(sizes[iter])) particles finished in $time_string\n")
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end
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end
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plot(n_particles_vec, times,
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xaxis = :log, yaxis = :log,
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xticks = (n_particles_vec, n_particles_vec),
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xlabel = "#particles", ylabel = "Runtime [s]",
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legend = :outerright, size = (750, 400), dpi = 200,
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label = neighborhood_searches_names, title = title)
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end

‎test/Project.toml‎

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[deps]
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BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
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Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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‎test/benchmarks.jl‎

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# Check that all benchmarks are running without errors.
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# Note that these are only smoke tests, not verifying the result.
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# Also note that these tests are run without coverage checks, since we want to
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# cover everything with unit tests.
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@testset verbose=true "Benchmarks" begin
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include("../benchmarks/benchmarks.jl")
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@testset verbose=true "$(length(size))D" for size in [(50,), (10, 10), (5, 5, 5)]
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@testset verbose=true "`benchmark_count_neighbors`" begin
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@test_nowarn_mod plot_benchmarks(benchmark_count_neighbors, size, 2)
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end
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@testset verbose=true "`benchmark_n_body`" begin
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@test_nowarn_mod plot_benchmarks(benchmark_n_body, size, 2)
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end
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end
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end;

‎test/runtests.jl‎

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include("test_util.jl")
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const POINTNEIGHBORS_TEST = lowercase(get(ENV, "POINTNEIGHBORS_TEST", "all"))
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@testset verbose=true "PointNeighbors.jl Tests" begin
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include("nhs_trivial.jl")
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include("nhs_grid.jl")
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include("neighborhood_search.jl")
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if POINTNEIGHBORS_TEST in ("all", "unit")
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include("unittest.jl")
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end
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if POINTNEIGHBORS_TEST in ("all", "benchmarks")
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include("benchmarks.jl")
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end
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end;

‎test/unittest.jl‎

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# Separate file that can be executed to only run unit tests.
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# Include `test_util.jl` first.
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@testset verbose=true "Unit Tests" begin
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include("nhs_trivial.jl")
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include("nhs_grid.jl")
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include("neighborhood_search.jl")
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end;

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