mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Sameer Agarwal
0258b2a0b0
The sizes are reflective of the matrix sizes that occur in production. Change-Id: I8cd85d0640702df09afafced0d28ef416bab946f
115 lines
4.3 KiB
C++
115 lines
4.3 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2018 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Authors: sameeragarwal@google.com (Sameer Agarwal)
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#include "Eigen/Dense"
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#include "benchmark/benchmark.h"
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#include "ceres/small_blas.h"
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namespace ceres {
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// Benchmarking matrix-vector multiply routines and optimizing memory
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// access requires that we make sure that they are not just sitting in
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// the cache. So, as the benchmarking routine iterates, we need to
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// multiply new/different matrice and vectors. Allocating/creating
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// these objects in the benchmarking loop is too heavy duty, so we
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// create them before hand and cycle through them in the
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// benchmark. This class, given the size of the matrix creates such
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// matrix and vector objects for use in the benchmark.
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class MatrixVectorMultiplyData {
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public:
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MatrixVectorMultiplyData(int rows, int cols)
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: num_elements_(1000),
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rows_(rows),
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cols_(cols),
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a_(num_elements_ * rows, 1.001),
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b_(num_elements_ * rows * cols, 1.5),
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c_(num_elements_ * cols, 1.00003) {}
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int num_elements() const { return num_elements_; }
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double* GetA(int i) { return &a_[i * rows_]; }
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double* GetB(int i) { return &b_[i * rows_ * cols_]; }
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double* GetC(int i) { return &c_[i * cols_]; }
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private:
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const int num_elements_;
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const int rows_;
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const int cols_;
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std::vector<double> a_;
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std::vector<double> b_;
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std::vector<double> c_;
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};
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// Helper function to generate the various matrix sizes for which we
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// run the benchmark.
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static void MatrixSizeArguments(benchmark::internal::Benchmark* benchmark) {
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std::vector<int> rows = {1, 2, 3, 4, 6, 8};
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std::vector<int> cols = {1, 2, 3, 4, 8, 12, 15};
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for (int r : rows) {
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for (int c : cols) {
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benchmark->Args({r, c});
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}
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}
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}
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void BM_MatrixVectorMultiply(benchmark::State& state) {
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const int rows = state.range(0);
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const int cols = state.range(1);
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MatrixVectorMultiplyData data(rows, cols);
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const int num_elements = data.num_elements();
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int iter = 0;
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for (auto _ : state) {
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// A += B * C;
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internal::MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
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data.GetB(iter), rows, cols, data.GetC(iter), data.GetA(iter));
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iter = (iter + 1) % num_elements;
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}
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}
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BENCHMARK(BM_MatrixVectorMultiply)->Apply(MatrixSizeArguments);
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void BM_MatrixTransposeVectorMultiply(benchmark::State& state) {
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const int rows = state.range(0);
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const int cols = state.range(1);
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MatrixVectorMultiplyData data(cols, rows);
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const int num_elements = data.num_elements();
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int iter = 0;
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for (auto _ : state) {
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internal::MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
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data.GetB(iter), rows, cols, data.GetC(iter), data.GetA(iter));
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iter = (iter + 1) % num_elements;
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}
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}
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BENCHMARK(BM_MatrixTransposeVectorMultiply)->Apply(MatrixSizeArguments);
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} // namespace ceres
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BENCHMARK_MAIN();
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