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add script to benchmark KKT systems solution time for OPF (#70)
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include("common.jl") | ||
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#= | ||
CONFIG | ||
=# | ||
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# Number of trial runs to estimate running time. | ||
ntrials = 3 | ||
# Save results on disk? | ||
save_results = true | ||
# Should we use the GPU to evaluate the derivatives? | ||
use_gpu = true | ||
# Verbose level | ||
verbose = true | ||
print_level = if verbose | ||
MadNLP.DEBUG | ||
else | ||
MadNLP.ERROR | ||
end | ||
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# OPF instances | ||
cases = [ | ||
"case118.m", | ||
"case1354pegase.m", | ||
"case2869pegase.m", | ||
"case9241pegase.m", | ||
] | ||
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function benchmark_kkt(model, kkt; use_gpu=false, ntrials=3, options...) | ||
use_gpu && refresh_memory() | ||
blk = build_opf_model(model; use_gpu=use_gpu) | ||
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## Warm-up | ||
solver = build_madnlp(blk, kkt; max_iter=1, options...) | ||
MadNLP.solve!(solver) | ||
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## Benchmark | ||
t_build, t_factorization, t_backsolve = (0.0, 0.0, 0.0) | ||
delta_err = 0.0 | ||
for _ in 1:ntrials | ||
t_build += CUDA.@elapsed begin | ||
MadNLP.build_kkt!(solver.kkt) | ||
end | ||
t_factorization += CUDA.@elapsed begin | ||
MadNLP.factorize!(solver.linear_solver) | ||
end | ||
t_backsolve += CUDA.@elapsed begin | ||
MadNLP.solve_refine_wrapper!(solver, solver.d, solver.p) | ||
end | ||
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dsol = MadNLP.primal_dual(solver.d) | ||
n = length(dsol) | ||
psol = zeros(n) | ||
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mul!(psol, solver.kkt, dsol) | ||
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delta_err += norm(psol .- MadNLP.primal_dual(solver.p), Inf) | ||
end | ||
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return ( | ||
build=t_build / ntrials, | ||
factorization=t_factorization / ntrials, | ||
backsolve=t_backsolve / ntrials, | ||
accuracy=delta_err / ntrials, | ||
) | ||
end | ||
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function benchmark_kkt(cases, kkt, ntrials, save_results; use_gpu=false, options...) | ||
# Setup | ||
dev = use_gpu ? :cuda : :cpu | ||
form = isa(kkt, Argos.BieglerReduction) ? :biegler : :full | ||
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nexp = length(cases) | ||
results = zeros(nexp, 5) | ||
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i = 0 | ||
for case in cases | ||
i += 1 | ||
datafile = joinpath(DATA, case) | ||
model = ExaPF.PolarForm(datafile) | ||
nbus = PS.get(model, PS.NumberOfBuses()) | ||
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r = benchmark_kkt(model, kkt; ntrials=ntrials, use_gpu=use_gpu, options...) | ||
results[i, :] .= (nbus, r.build, r.factorization, r.backsolve, r.accuracy) | ||
end | ||
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if save_results | ||
output_dir = joinpath(dirname(@__FILE__), RESULTS_DIR) | ||
if !isdir(output_dir) | ||
mkdir(output_dir) | ||
end | ||
output_file = joinpath(output_dir, "benchmark_kkt_$(form)_$(dev).txt") | ||
writedlm(output_file, results) | ||
end | ||
return results | ||
end | ||
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#= | ||
Benchmark using ma27 as a reference. | ||
=# | ||
benchmark_kkt( | ||
cases, | ||
Argos.FullSpace(), | ||
ntrials, | ||
save_results; | ||
print_level=print_level, | ||
linear_solver=Ma27Solver, | ||
use_gpu=use_gpu, | ||
) | ||
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#= | ||
Benchmark Biegler's reduction. | ||
=# | ||
benchmark_kkt( | ||
cases, | ||
Argos.BieglerReduction(), | ||
ntrials, | ||
save_results; | ||
print_level=print_level, | ||
linear_solver=LapackGPUSolver, | ||
use_gpu=use_gpu, | ||
) | ||
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using DelimitedFiles | ||
using LazyArtifacts | ||
using LinearAlgebra | ||
using Printf | ||
using Random | ||
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using NLPModels | ||
using Argos | ||
using ExaPF | ||
using MadNLP | ||
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# HSL | ||
using MadNLPHSL | ||
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# GPU | ||
using CUDA | ||
using KernelAbstractions | ||
using ArgosCUDA | ||
using MadNLPGPU | ||
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const PS = ExaPF.PowerSystem | ||
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const DATA = joinpath(artifact"ExaData", "ExaData") | ||
RESULTS_DIR = "results" | ||
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if CUDA.has_cuda() | ||
CUDA.allowscalar(false) | ||
end | ||
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function refresh_memory() | ||
GC.gc(true) | ||
CUDA.has_cuda() && CUDA.reclaim() | ||
return | ||
end | ||
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function init_model!(blk) | ||
x0 = NLPModels.get_x0(blk) | ||
nnzj = NLPModels.get_nnzj(blk) | ||
jac = zeros(nnzj) | ||
NLPModels.jac_coord!(blk, x0, jac) | ||
return | ||
end | ||
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function build_opf_model(model; use_gpu=false) | ||
if use_gpu | ||
model_gpu = PolarForm(model, CUDABackend()) | ||
nlp = Argos.FullSpaceEvaluator(model_gpu) | ||
blk = Argos.OPFModel(Argos.bridge(nlp)) | ||
else | ||
nlp = Argos.FullSpaceEvaluator(model) | ||
blk = Argos.OPFModel(nlp) | ||
end | ||
init_model!(blk) | ||
return blk | ||
end | ||
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function build_madnlp( | ||
blk::Argos.OPFModel, | ||
::Argos.FullSpace; | ||
max_iter=max_iter, | ||
dual_initialized=true, | ||
tol=1e-5, | ||
print_level=MadNLP.ERROR, | ||
linear_solver=Ma27Solver, | ||
) | ||
return MadNLP.MadNLPSolver(blk; max_iter=max_iter, dual_initialized=dual_initialized, tol=tol, print_level=print_level, linear_solver=linear_solver) | ||
end | ||
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function build_madnlp( | ||
blk::Argos.OPFModel, | ||
::Argos.BieglerReduction; | ||
max_iter=max_iter, | ||
dual_initialized=true, | ||
tol=1e-5, | ||
print_level=MadNLP.ERROR, | ||
linear_solver=nothing, | ||
) | ||
madnlp_options = Dict{Symbol, Any}() | ||
madnlp_options[:linear_solver] = LapackGPUSolver | ||
madnlp_options[:lapack_algorithm] = MadNLP.CHOLESKY | ||
madnlp_options[:dual_initialized] = dual_initialized | ||
madnlp_options[:max_iter] = max_iter | ||
madnlp_options[:print_level] = print_level | ||
madnlp_options[:tol] = tol | ||
opt_ipm, opt_linear, logger = MadNLP.load_options(; madnlp_options...) | ||
KKT = Argos.BieglerKKTSystem{Float64, CuVector{Int}, CuVector{Float64}, CuMatrix{Float64}} | ||
return MadNLP.MadNLPSolver{Float64, KKT}(blk, opt_ipm, opt_linear; logger=logger) | ||
end | ||
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