mirror of
https://gitlab.com/libeigen/eigen.git
synced 2026-04-10 11:34:33 +08:00
130 lines
4.8 KiB
C++
130 lines
4.8 KiB
C++
// Benchmarks for LU decompositions.
|
|
//
|
|
// Tests PartialPivLU and FullPivLU: compute, solve, inverse, determinant.
|
|
|
|
#include <benchmark/benchmark.h>
|
|
#include <Eigen/LU>
|
|
|
|
using namespace Eigen;
|
|
|
|
typedef Matrix<float, Dynamic, Dynamic> Matf;
|
|
typedef Matrix<double, Dynamic, Dynamic> Matd;
|
|
|
|
// --- PartialPivLU ---
|
|
|
|
template <typename Scalar>
|
|
EIGEN_DONT_INLINE void do_compute(PartialPivLU<Matrix<Scalar, Dynamic, Dynamic>>& lu,
|
|
const Matrix<Scalar, Dynamic, Dynamic>& A) {
|
|
lu.compute(A);
|
|
}
|
|
|
|
template <typename Scalar>
|
|
static void BM_PartialPivLU_Compute(benchmark::State& state) {
|
|
const Index n = state.range(0);
|
|
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
|
|
Mat A = Mat::Random(n, n);
|
|
PartialPivLU<Mat> lu(n);
|
|
for (auto _ : state) {
|
|
do_compute(lu, A);
|
|
benchmark::DoNotOptimize(lu.matrixLU().data());
|
|
}
|
|
state.SetItemsProcessed(state.iterations());
|
|
}
|
|
|
|
template <typename Scalar>
|
|
static void BM_PartialPivLU_Solve(benchmark::State& state) {
|
|
const Index n = state.range(0);
|
|
const Index nrhs = state.range(1);
|
|
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
|
|
Mat A = Mat::Random(n, n);
|
|
Mat B = Mat::Random(n, nrhs);
|
|
PartialPivLU<Mat> lu(A);
|
|
Mat X(n, nrhs);
|
|
for (auto _ : state) {
|
|
X = lu.solve(B);
|
|
benchmark::DoNotOptimize(X.data());
|
|
}
|
|
state.SetItemsProcessed(state.iterations());
|
|
}
|
|
|
|
template <typename Scalar>
|
|
static void BM_PartialPivLU_Inverse(benchmark::State& state) {
|
|
const Index n = state.range(0);
|
|
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
|
|
Mat A = Mat::Random(n, n);
|
|
PartialPivLU<Mat> lu(A);
|
|
Mat inv(n, n);
|
|
for (auto _ : state) {
|
|
inv = lu.inverse();
|
|
benchmark::DoNotOptimize(inv.data());
|
|
}
|
|
state.SetItemsProcessed(state.iterations());
|
|
}
|
|
|
|
template <typename Scalar>
|
|
static void BM_PartialPivLU_Determinant(benchmark::State& state) {
|
|
const Index n = state.range(0);
|
|
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
|
|
Mat A = Mat::Random(n, n);
|
|
PartialPivLU<Mat> lu(A);
|
|
for (auto _ : state) {
|
|
Scalar d = lu.determinant();
|
|
benchmark::DoNotOptimize(d);
|
|
}
|
|
state.SetItemsProcessed(state.iterations());
|
|
}
|
|
|
|
// --- FullPivLU ---
|
|
|
|
template <typename Scalar>
|
|
EIGEN_DONT_INLINE void do_compute(FullPivLU<Matrix<Scalar, Dynamic, Dynamic>>& lu,
|
|
const Matrix<Scalar, Dynamic, Dynamic>& A) {
|
|
lu.compute(A);
|
|
}
|
|
|
|
template <typename Scalar>
|
|
static void BM_FullPivLU_Compute(benchmark::State& state) {
|
|
const Index n = state.range(0);
|
|
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
|
|
Mat A = Mat::Random(n, n);
|
|
FullPivLU<Mat> lu(n, n);
|
|
for (auto _ : state) {
|
|
do_compute(lu, A);
|
|
benchmark::DoNotOptimize(lu.matrixLU().data());
|
|
}
|
|
state.SetItemsProcessed(state.iterations());
|
|
}
|
|
|
|
template <typename Scalar>
|
|
static void BM_FullPivLU_Solve(benchmark::State& state) {
|
|
const Index n = state.range(0);
|
|
const Index nrhs = state.range(1);
|
|
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
|
|
Mat A = Mat::Random(n, n);
|
|
Mat B = Mat::Random(n, nrhs);
|
|
FullPivLU<Mat> lu(A);
|
|
Mat X(n, nrhs);
|
|
for (auto _ : state) {
|
|
X = lu.solve(B);
|
|
benchmark::DoNotOptimize(X.data());
|
|
}
|
|
state.SetItemsProcessed(state.iterations());
|
|
}
|
|
|
|
// --- Size configurations ---
|
|
|
|
// clang-format off
|
|
BENCHMARK(BM_PartialPivLU_Compute<float>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("PartialPivLU_Compute_float");
|
|
BENCHMARK(BM_PartialPivLU_Compute<double>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("PartialPivLU_Compute_double");
|
|
BENCHMARK(BM_PartialPivLU_Solve<float>)->ArgsProduct({{32, 128, 512, 1024}, {1, 16, 64}})->Name("PartialPivLU_Solve_float");
|
|
BENCHMARK(BM_PartialPivLU_Solve<double>)->ArgsProduct({{32, 128, 512, 1024}, {1, 16, 64}})->Name("PartialPivLU_Solve_double");
|
|
BENCHMARK(BM_PartialPivLU_Inverse<float>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("PartialPivLU_Inverse_float");
|
|
BENCHMARK(BM_PartialPivLU_Inverse<double>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("PartialPivLU_Inverse_double");
|
|
BENCHMARK(BM_PartialPivLU_Determinant<float>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("PartialPivLU_Determinant_float");
|
|
BENCHMARK(BM_PartialPivLU_Determinant<double>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("PartialPivLU_Determinant_double");
|
|
BENCHMARK(BM_FullPivLU_Compute<float>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("FullPivLU_Compute_float");
|
|
BENCHMARK(BM_FullPivLU_Compute<double>)->Arg(8)->Arg(32)->Arg(64)->Arg(128)->Arg(256)->Arg(512)->Arg(1024)->Name("FullPivLU_Compute_double");
|
|
BENCHMARK(BM_FullPivLU_Solve<float>)->ArgsProduct({{32, 128, 512, 1024}, {1, 16, 64}})->Name("FullPivLU_Solve_float");
|
|
BENCHMARK(BM_FullPivLU_Solve<double>)->ArgsProduct({{32, 128, 512, 1024}, {1, 16, 64}})->Name("FullPivLU_Solve_double");
|
|
// clang-format on
|