qmax Determination
qs = 1.0 .+ 2.0.^(-5:2)
times = Array{Float64}(undef,length(qs),4)
means = Array{Float64}(undef,length(qs),4)
using StochasticDiffEq, SDEProblemLibrary, Random,
Plots, ParallelDataTransfer, DiffEqMonteCarlo, Distributed
Random.seed!(99)
full_prob = SDEProblemLibrary.oval2ModelExample(largeFluctuations=true,useBigs=false)
import SDEProblemLibrary: prob_sde_additivesystem,
prob_sde_additive, prob_sde_2Dlinear, prob_sde_linear, prob_sde_wave
prob = remake(full_prob,tspan=(0.0,1.0))
println("Solve once to compile.")
sol = solve(prob,EM(),dt=1/2^(18))
Int(sol.u[end][1]!=NaN)
println("Compilation complete.")
num_runs = 10000
probs = Vector{SDEProblem}(undef,3)
p1 = Vector{Any}(undef,3)
p2 = Vector{Any}(undef,3)
p3 = Vector{Any}(undef,3)
## Problem 1
probs[1] = prob_sde_linear
## Problem 2
probs[2] = prob_sde_wave
## Problem 3
probs[3] = prob_sde_additive
println("Setup Complete")
## Timing Runs
function runAdaptive(i,k)
sol = solve(prob,SRIW1(),dt=1/2^(8),abstol=2.0^(-15),reltol=2.0^(-10),
verbose=false,maxIters=Int(1e12),qmax=qs[k])
Int(any(isnan,sol[end]) || sol.t[end] != 1)
end
#Compile
monte_prob = EnsembleProblem(probs[1])
test_mc = solve(monte_prob,SRIW1(),dt=1/2^(4),adaptive=true,trajectories=1000,abstol=2.0^(-1),reltol=0)
DiffEqBase.calculate_monte_errors(test_mc);
Error: On worker 2:
ArgumentError: Package SDEProblemLibrary [c72e72a9-a271-4b2b-8966-303ed9567
72e] is required but does not seem to be installed:
- Run `Pkg.instantiate()` to install all recorded dependencies.
Stacktrace:
[1] _require
@ ./loading.jl:1089
[2] require
@ ./loading.jl:1013
[3] #2
@ /cache/julia-buildkite-plugin/julia_installs/bin/linux/x64/1.7/julia-1
.7-latest-linux-x86_64/share/julia/stdlib/v1.7/Distributed/src/Distributed.
jl:79
[4] #114
@ /cache/julia-buildkite-plugin/julia_installs/bin/linux/x64/1.7/julia-1
.7-latest-linux-x86_64/share/julia/stdlib/v1.7/Distributed/src/process_mess
ages.jl:294
[5] run_work_thunk
@ /cache/julia-buildkite-plugin/julia_installs/bin/linux/x64/1.7/julia-1
.7-latest-linux-x86_64/share/julia/stdlib/v1.7/Distributed/src/process_mess
ages.jl:63
[6] run_work_thunk
@ /cache/julia-buildkite-plugin/julia_installs/bin/linux/x64/1.7/julia-1
.7-latest-linux-x86_64/share/julia/stdlib/v1.7/Distributed/src/process_mess
ages.jl:72
[7] #100
@ ./task.jl:429
...and 1 more exception.
qmax test on Oval2 Model
for k in eachindex(qs)
global times
Random.seed!(99)
adaptiveTime = @elapsed numFails = sum(map((i)->runAdaptive(i,k),1:num_runs))
println("k was $k. The number of Adaptive Fails is $numFails. Elapsed time was $adaptiveTime")
times[k,4] = adaptiveTime
end
Error: UndefVarError: Random not defined
qmax test on other problems
for k in eachindex(probs)
global probs, times, means, qs
println("Problem $k")
## Setup
prob = probs[k]
for i in eachindex(qs)
msim = solve(monte_prob,dt=1/2^(4),SRIW1(),adaptive=true,trajectories=num_runs,abstol=2.0^(-13),reltol=0,qmax=qs[i])
test_msim = DiffEqBase.calculate_monte_errors(msim)
times[i,k] = test_msim.elapsedTime
means[i,k] = test_msim.error_means[:final]
println("for k=$k and i=$i, we get that the error was $(means[i,k]) and it took $(times[i,k]) seconds")
end
end
Error: UndefVarError: probs not defined
Appendix
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/AdaptiveSDE","qmaxDetermination.jmd")
Computer Information:
Julia Version 1.7.3
Commit 742b9abb4d (2022-05-06 12:58 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: AMD EPYC 7502 32-Core Processor
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-12.0.1 (ORCJIT, znver2)
Environment:
JULIA_CPU_THREADS = 128
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-amdci1-0/julialang/scimlbenchmarks-dot-jl/benchmarks/AdaptiveSDE/Project.toml`
[77a26b50] DiffEqNoiseProcess v5.13.0
[2dcacdae] ParallelDataTransfer v0.5.0
[91a5bcdd] Plots v1.34.0
[c72e72a9] SDEProblemLibrary v0.1.3
[31c91b34] SciMLBenchmarks v0.1.1
[789caeaf] StochasticDiffEq v6.54.0
[8ba89e20] Distributed
[9a3f8284] Random
And the full manifest:
Status `/cache/build/exclusive-amdci1-0/julialang/scimlbenchmarks-dot-jl/benchmarks/AdaptiveSDE/Manifest.toml`
[c3fe647b] AbstractAlgebra v0.27.4
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[14a3606d] MozillaCACerts_jll v2022.2.1
[4536629a] OpenBLAS_jll v0.3.20+0
[05823500] OpenLibm_jll v0.8.1+0
[efcefdf7] PCRE2_jll v10.40.0+0
[bea87d4a] SuiteSparse_jll v5.10.1+0
[83775a58] Zlib_jll v1.2.12+3
[8e850b90] libblastrampoline_jll v5.1.1+0
[8e850ede] nghttp2_jll v1.48.0+0
[3f19e933] p7zip_jll v17.4.0+0