// 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 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // 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 static void BM_DenseSolver(benchmark::State& state) { const int num_rows = static_cast(state.range(0)); const int num_cols = static_cast(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();