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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2018 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
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// POSSIBILITY OF SUCH DAMAGE.
//
// Authors: sameeragarwal@google.com (Sameer Agarwal)
#include "Eigen/Dense"
#include "benchmark/benchmark.h"
#include "ceres/small_blas.h"
namespace ceres {
// Benchmarking matrix-vector multiply routines and optimizing memory
// access requires that we make sure that they are not just sitting in
// the cache. So, as the benchmarking routine iterates, we need to
// multiply new/different matrice and vectors. Allocating/creating
// these objects in the benchmarking loop is too heavy duty, so we
// create them before hand and cycle through them in the
// benchmark. This class, given the size of the matrix creates such
// matrix and vector objects for use in the benchmark.
class MatrixVectorMultiplyData {
public:
MatrixVectorMultiplyData(int rows, int cols)
: num_elements_(1000),
rows_(rows),
cols_(cols),
a_(num_elements_ * rows, 1.001),
b_(num_elements_ * rows * cols, 1.5),
c_(num_elements_ * cols, 1.00003) {}
int num_elements() const { return num_elements_; }
double* GetA(int i) { return &a_[i * rows_]; }
double* GetB(int i) { return &b_[i * rows_ * cols_]; }
double* GetC(int i) { return &c_[i * cols_]; }
private:
const int num_elements_;
const int rows_;
const int cols_;
std::vector<double> a_;
std::vector<double> b_;
std::vector<double> c_;
};
// Helper function to generate the various matrix sizes for which we
// run the benchmark.
static void MatrixSizeArguments(benchmark::internal::Benchmark* benchmark) {
std::vector<int> rows = {1, 2, 3, 4, 6, 8};
std::vector<int> cols = {1, 2, 3, 4, 8, 12, 15};
for (int r : rows) {
for (int c : cols) {
benchmark->Args({r, c});
}
}
}
static void BM_MatrixVectorMultiply(benchmark::State& state) {
const int rows = state.range(0);
const int cols = state.range(1);
MatrixVectorMultiplyData data(rows, cols);
const int num_elements = data.num_elements();
int iter = 0;
for (auto _ : state) {
// A += B * C;
internal::MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
data.GetB(iter), rows, cols, data.GetC(iter), data.GetA(iter));
iter = (iter + 1) % num_elements;
}
}
BENCHMARK(BM_MatrixVectorMultiply)->Apply(MatrixSizeArguments);
static void BM_MatrixTransposeVectorMultiply(benchmark::State& state) {
const int rows = state.range(0);
const int cols = state.range(1);
MatrixVectorMultiplyData data(cols, rows);
const int num_elements = data.num_elements();
int iter = 0;
for (auto _ : state) {
internal::MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
data.GetB(iter), rows, cols, data.GetC(iter), data.GetA(iter));
iter = (iter + 1) % num_elements;
}
}
BENCHMARK(BM_MatrixTransposeVectorMultiply)->Apply(MatrixSizeArguments);
} // namespace ceres
BENCHMARK_MAIN();