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Add benchmarks for unsupported modules and extend supported benchmarks

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libeigen/eigen!2179

Closes #3036

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
This commit is contained in:
Rasmus Munk Larsen
2026-02-24 17:12:33 -08:00
parent fa567f6bcd
commit 16da0279f1
33 changed files with 2320 additions and 10 deletions
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98
unsupported/benchmarks/Splines/bench_splines.cpp Normal file
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// Benchmarks for Eigen Spline module.
// Tests fitting, evaluation, and derivative computation.
#include <benchmark/benchmark.h>
#include <Eigen/Core>
#include <unsupported/Eigen/Splines>
using namespace Eigen;
typedef double Scalar;
// --- Spline fitting (interpolation) ---
template <int Dim, int Degree>
static void BM_SplineFit(benchmark::State& state) {
const int n = state.range(0);
typedef Spline<Scalar, Dim> SplineType;
typedef typename SplineType::PointType PointType;
// Generate random points.
Matrix<Scalar, Dim, Dynamic> pts(Dim, n);
pts.setRandom();
for (auto _ : state) {
SplineType spline = SplineFitting<SplineType>::Interpolate(pts, Degree);
benchmark::DoNotOptimize(spline.knots().data());
benchmark::ClobberMemory();
}
}
// --- Spline evaluation ---
template <int Dim, int Degree>
static void BM_SplineEval(benchmark::State& state) {
const int n = state.range(0); // number of control points for fitting
const int neval = 1000; // number of evaluation points
typedef Spline<Scalar, Dim> SplineType;
Matrix<Scalar, Dim, Dynamic> pts(Dim, n);
pts.setRandom();
SplineType spline = SplineFitting<SplineType>::Interpolate(pts, Degree);
// Generate evaluation parameters in [0, 1].
VectorXd u = VectorXd::LinSpaced(neval, 0, 1);
for (auto _ : state) {
for (int i = 0; i < neval; ++i) {
auto pt = spline(u(i));
benchmark::DoNotOptimize(pt.data());
}
benchmark::ClobberMemory();
}
state.counters["Evals/s"] = benchmark::Counter(neval, benchmark::Counter::kIsIterationInvariantRate);
}
// --- Spline derivative evaluation ---
template <int Dim, int Degree>
static void BM_SplineDerivatives(benchmark::State& state) {
const int n = state.range(0);
const int neval = 1000;
typedef Spline<Scalar, Dim> SplineType;
Matrix<Scalar, Dim, Dynamic> pts(Dim, n);
pts.setRandom();
SplineType spline = SplineFitting<SplineType>::Interpolate(pts, Degree);
VectorXd u = VectorXd::LinSpaced(neval, 0, 1);
for (auto _ : state) {
for (int i = 0; i < neval; ++i) {
auto derivs = spline.derivatives(u(i), 1);
benchmark::DoNotOptimize(derivs.data());
}
benchmark::ClobberMemory();
}
state.counters["Evals/s"] = benchmark::Counter(neval, benchmark::Counter::kIsIterationInvariantRate);
}
static void SplineSizes(::benchmark::Benchmark* b) {
for (int n : {10, 50, 200, 1000}) {
b->Arg(n);
}
}
// 2D cubic splines
BENCHMARK(BM_SplineFit<2, 3>)->Apply(SplineSizes)->Name("SplineFit_2D_Cubic");
BENCHMARK(BM_SplineEval<2, 3>)->Apply(SplineSizes)->Name("SplineEval_2D_Cubic");
BENCHMARK(BM_SplineDerivatives<2, 3>)->Apply(SplineSizes)->Name("SplineDerivatives_2D_Cubic");
// 3D cubic splines
BENCHMARK(BM_SplineFit<3, 3>)->Apply(SplineSizes)->Name("SplineFit_3D_Cubic");
BENCHMARK(BM_SplineEval<3, 3>)->Apply(SplineSizes)->Name("SplineEval_3D_Cubic");
BENCHMARK(BM_SplineDerivatives<3, 3>)->Apply(SplineSizes)->Name("SplineDerivatives_3D_Cubic");
// 2D quintic splines
BENCHMARK(BM_SplineFit<2, 5>)->Apply(SplineSizes)->Name("SplineFit_2D_Quintic");
BENCHMARK(BM_SplineEval<2, 5>)->Apply(SplineSizes)->Name("SplineEval_2D_Quintic");