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Add new benchmarks for Core, LU, and QR operations

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libeigen/eigen!2177

Closes #3035

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
This commit is contained in:
Rasmus Munk Larsen
2026-02-22 12:19:37 -08:00
parent 8c35441f18
commit 1f49bf96cf
15 changed files with 1355 additions and 2 deletions
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81
benchmarks/Core/bench_diagonal.cpp Normal file
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// Benchmarks for diagonal operations.
//
// Tests diagonal extraction, diagonal-matrix product, and matrix-diagonal product.
#include <benchmark/benchmark.h>
#include <Eigen/Core>
using namespace Eigen;
// Extract diagonal from a square matrix and sum it.
template <typename Scalar>
static void BM_DiagonalExtract(benchmark::State& state) {
const Index n = state.range(0);
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
Mat A = Mat::Random(n, n);
for (auto _ : state) {
Scalar s = A.diagonal().sum();
benchmark::DoNotOptimize(s);
}
state.SetBytesProcessed(state.iterations() * n * sizeof(Scalar));
}
// y = diag(d) * x (diagonal matrix times vector).
template <typename Scalar>
static void BM_DiagonalTimesVector(benchmark::State& state) {
const Index n = state.range(0);
using Vec = Matrix<Scalar, Dynamic, 1>;
Vec d = Vec::Random(n);
Vec x = Vec::Random(n);
Vec y(n);
for (auto _ : state) {
y = d.asDiagonal() * x;
benchmark::DoNotOptimize(y.data());
}
state.SetBytesProcessed(state.iterations() * 3 * n * sizeof(Scalar));
}
// C = diag(d) * A (diagonal matrix times dense matrix).
template <typename Scalar>
static void BM_DiagonalTimesMatrix(benchmark::State& state) {
const Index n = state.range(0);
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
using Vec = Matrix<Scalar, Dynamic, 1>;
Vec d = Vec::Random(n);
Mat A = Mat::Random(n, n);
Mat C(n, n);
for (auto _ : state) {
C.noalias() = d.asDiagonal() * A;
benchmark::DoNotOptimize(C.data());
}
state.SetBytesProcessed(state.iterations() * 2 * n * n * sizeof(Scalar));
}
// C = A * diag(d) (dense matrix times diagonal matrix).
template <typename Scalar>
static void BM_MatrixTimesDiagonal(benchmark::State& state) {
const Index n = state.range(0);
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
using Vec = Matrix<Scalar, Dynamic, 1>;
Vec d = Vec::Random(n);
Mat A = Mat::Random(n, n);
Mat C(n, n);
for (auto _ : state) {
C.noalias() = A * d.asDiagonal();
benchmark::DoNotOptimize(C.data());
}
state.SetBytesProcessed(state.iterations() * 2 * n * n * sizeof(Scalar));
}
static void Sizes(::benchmark::Benchmark* b) {
for (int n : {32, 64, 128, 256, 512, 1024}) b->Arg(n);
}
BENCHMARK(BM_DiagonalExtract<float>)->Apply(Sizes)->Name("DiagonalExtract_float");
BENCHMARK(BM_DiagonalExtract<double>)->Apply(Sizes)->Name("DiagonalExtract_double");
BENCHMARK(BM_DiagonalTimesVector<float>)->Apply(Sizes)->Name("DiagonalTimesVector_float");
BENCHMARK(BM_DiagonalTimesVector<double>)->Apply(Sizes)->Name("DiagonalTimesVector_double");
BENCHMARK(BM_DiagonalTimesMatrix<float>)->Apply(Sizes)->Name("DiagonalTimesMatrix_float");
BENCHMARK(BM_DiagonalTimesMatrix<double>)->Apply(Sizes)->Name("DiagonalTimesMatrix_double");
BENCHMARK(BM_MatrixTimesDiagonal<float>)->Apply(Sizes)->Name("MatrixTimesDiagonal_float");
BENCHMARK(BM_MatrixTimesDiagonal<double>)->Apply(Sizes)->Name("MatrixTimesDiagonal_double");