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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Authors: sameeragarwal@google.com (Sameer Agarwal)
#include "Eigen/Dense"
#include "benchmark/benchmark.h"
#include "ceres/context_impl.h"
#include "ceres/dense_sparse_matrix.h"
#include "ceres/internal/config.h"
#include "ceres/linear_solver.h"
namespace ceres::internal {
template <ceres::DenseLinearAlgebraLibraryType kLibraryType,
ceres::LinearSolverType kSolverType>
static void BM_DenseSolver(benchmark::State& state) {
const int num_rows = static_cast<int>(state.range(0));
const int num_cols = static_cast<int>(state.range(1));
DenseSparseMatrix jacobian(num_rows, num_cols);
*jacobian.mutable_matrix() = Eigen::MatrixXd::Random(num_rows, num_cols);
Eigen::VectorXd rhs = Eigen::VectorXd::Random(num_rows, 1);
Eigen::VectorXd solution(num_cols);
LinearSolver::Options options;
options.type = kSolverType;
options.dense_linear_algebra_library_type = kLibraryType;
ContextImpl context;
options.context = &context;
auto solver = LinearSolver::Create(options);
LinearSolver::PerSolveOptions per_solve_options;
Eigen::VectorXd diagonal = Eigen::VectorXd::Ones(num_cols) * 100;
per_solve_options.D = diagonal.data();
for (auto _ : state) {
solver->Solve(&jacobian, rhs.data(), per_solve_options, solution.data());
}
}
// Some reasonable matrix sizes. I picked them out of thin air.
static void MatrixSizes(benchmark::internal::Benchmark* b) {
// {num_rows, num_cols}
b->Args({1, 1});
b->Args({2, 1});
b->Args({3, 1});
b->Args({6, 2});
b->Args({10, 3});
b->Args({12, 4});
b->Args({20, 5});
b->Args({40, 5});
b->Args({100, 10});
b->Args({150, 15});
b->Args({200, 16});
b->Args({225, 18});
b->Args({300, 20});
b->Args({400, 20});
b->Args({600, 22});
b->Args({800, 25});
}
BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::EIGEN, ceres::DENSE_QR)
->Apply(MatrixSizes);
BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::EIGEN, ceres::DENSE_NORMAL_CHOLESKY)
->Apply(MatrixSizes);
#ifndef CERES_NO_LAPACK
BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::LAPACK, ceres::DENSE_QR)
->Apply(MatrixSizes);
BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::LAPACK, ceres::DENSE_NORMAL_CHOLESKY)
->Apply(MatrixSizes);
#endif // CERES_NO_LAPACK
#ifndef CERES_NO_CUDA
BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::CUDA, ceres::DENSE_NORMAL_CHOLESKY)
->Apply(MatrixSizes);
BENCHMARK_TEMPLATE2(BM_DenseSolver, ceres::CUDA, ceres::DENSE_QR)
->Apply(MatrixSizes);
#endif // CERES_NO_CUDA
} // namespace ceres::internal
BENCHMARK_MAIN();