using DiffEqBase, Catalyst, DiffEqJump, DiffEqProblemLibrary.JumpProblemLibrary, Plots, Statistics, DataFrames gr() fmt = :png JumpProblemLibrary.importjumpproblems()
Here we implement a 1D continuous time random walk approximation of diffusion for $N$ lattice sites on $\left[0,1\right]$, with reflecting boundary conditions at $x=0$ and $x=1$.
N = 256 h = 1 / N u0 = 10*ones(Int64, N) tf = .01 methods = (Direct(),DirectFW(),SortingDirect(),NRM(),DirectCR(),RSSA()) shortlabels = [string(leg)[12:end-2] for leg in methods] jprob = prob_jump_diffnetwork prob = DiscreteProblem(u0, (0.0, tf), [1 / (h*h)]) rn = jprob.network(N) ploth = plot(reuse=false) for (i,method) in enumerate(methods) println("Benchmarking method: ", method) jump_prob = JumpProblem(rn, prob, method, save_positions=(false, false)) sol = solve(jump_prob, SSAStepper(), saveat=tf/1000.) plot!(ploth,sol.t,sol[Int(N//2),:],label=shortlabels[i], format=fmt) end plot!(ploth, title="Population at middle lattice site", xlabel="time",format=fmt)
Benchmarking method: DiffEqJump.Direct() Benchmarking method: DiffEqJump.DirectFW() Benchmarking method: DiffEqJump.SortingDirect() Benchmarking method: DiffEqJump.NRM() Benchmarking method: DiffEqJump.DirectCR() Benchmarking method: DiffEqJump.RSSA()
function run_benchmark!(t, jump_prob, stepper) sol = solve(jump_prob, stepper) @inbounds for i in 1:length(t) t[i] = @elapsed (sol = solve(jump_prob, stepper)) end end
run_benchmark! (generic function with 1 method)
nsims = 50 benchmarks = Vector{Vector{Float64}}() for method in methods jump_prob = JumpProblem(rn, prob, method, save_positions=(false, false)) stepper = SSAStepper() t = Vector{Float64}(undef,nsims) run_benchmark!(t, jump_prob, stepper) push!(benchmarks, t) end
medtimes = Vector{Float64}(undef,length(methods)) stdtimes = Vector{Float64}(undef,length(methods)) avgtimes = Vector{Float64}(undef,length(methods)) for i in 1:length(methods) medtimes[i] = median(benchmarks[i]) avgtimes[i] = mean(benchmarks[i]) stdtimes[i] = std(benchmarks[i]) end df = DataFrame(names=shortlabels,medtimes=medtimes,relmedtimes=(medtimes/medtimes[1]), avgtimes=avgtimes, std=stdtimes, cv=stdtimes./avgtimes)
6 rows × 6 columns
| names | medtimes | relmedtimes | avgtimes | std | cv | |
|---|---|---|---|---|---|---|
| String | Float64 | Float64 | Float64 | Float64 | Float64 | |
| 1 | Direct | 7.39065 | 1.0 | 7.39109 | 0.00727328 | 0.00098406 |
| 2 | DirectFW | 7.31383 | 0.989605 | 7.3234 | 0.0218754 | 0.00298706 |
| 3 | SortingDirect | 1.0762 | 0.145617 | 1.07679 | 0.00375535 | 0.00348755 |
| 4 | NRM | 0.818555 | 0.110756 | 0.819539 | 0.00338196 | 0.00412666 |
| 5 | DirectCR | 0.516422 | 0.069875 | 0.519925 | 0.0151209 | 0.0290829 |
| 6 | RSSA | 1.44363 | 0.195332 | 1.44395 | 0.00256909 | 0.00177921 |
sa = [string(round(mt,digits=4),"s") for mt in df.medtimes] bar(df.names,df.relmedtimes,legend=:false, fmt=fmt) scatter!(df.names, .05 .+ df.relmedtimes, markeralpha=0, series_annotations=sa, fmt=fmt) ylabel!("median relative to Direct") title!("256 Site 1D Diffusion CTRW")
These benchmarks are a part of the SciMLBenchmarks.jl repository, found at: https://github.com/SciML/SciMLBenchmarks.jl. For more information on high-performance scientific machine learning, check out the SciML Open Source Software Organization https://sciml.ai.
To locally run this benchmark, do the following commands:
using SciMLBenchmarks
SciMLBenchmarks.weave_file("benchmarks/Jumps","Diffusion_CTRW.jmd")Computer Information:
Julia Version 1.6.6
Commit b8708f954a (2022-03-28 07:17 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: AMD EPYC 7502 32-Core Processor
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-11.0.1 (ORCJIT, znver2)
Environment:
BUILDKITE_PLUGIN_JULIA_CACHE_DIR = /cache/julia-buildkite-plugin
JULIA_DEPOT_PATH = /cache/julia-buildkite-plugin/depots/5b300254-1738-4989-ae0a-f4d2d937f953
Package Information:
Status `/cache/build/exclusive-amdci3-0/julialang/scimlbenchmarks-dot-jl/benchmarks/Jumps/Project.toml`
[479239e8] Catalyst v6.13.0
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[31c91b34] SciMLBenchmarks v0.1.0
[10745b16] StatisticsAnd the full manifest:
Status `/cache/build/exclusive-amdci3-0/julialang/scimlbenchmarks-dot-jl/benchmarks/Jumps/Manifest.toml`
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