| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2022 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. |
| |
| #include <algorithm> |
| |
| #include "benchmark/benchmark.h" |
| #include "ceres/eigen_vector_ops.h" |
| #include "ceres/parallel_for.h" |
| |
| namespace ceres::internal { |
| |
| const int kVectorSize = 64 * 1024 * 1024 / sizeof(double); |
| |
| static void SetZero(benchmark::State& state) { |
| Vector x = Vector::Random(kVectorSize); |
| for (auto _ : state) { |
| x.setZero(); |
| } |
| CHECK_EQ(x.squaredNorm(), 0.); |
| } |
| BENCHMARK(SetZero); |
| |
| static void SetZeroParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| Vector x = Vector::Random(kVectorSize); |
| for (auto _ : state) { |
| ParallelSetZero(&context, num_threads, x); |
| } |
| CHECK_EQ(x.squaredNorm(), 0.); |
| } |
| BENCHMARK(SetZeroParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void Negate(benchmark::State& state) { |
| Vector x = Vector::Random(kVectorSize).normalized(); |
| const Vector x_init = x; |
| |
| for (auto _ : state) { |
| x = -x; |
| } |
| CHECK((x - x_init).squaredNorm() == 0. || (x + x_init).squaredNorm() == 0); |
| } |
| BENCHMARK(Negate); |
| |
| static void NegateParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| Vector x = Vector::Random(kVectorSize).normalized(); |
| const Vector x_init = x; |
| |
| for (auto _ : state) { |
| ParallelAssign(&context, num_threads, x, -x); |
| } |
| CHECK((x - x_init).squaredNorm() == 0. || (x + x_init).squaredNorm() == 0); |
| } |
| BENCHMARK(NegateParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void Assign(benchmark::State& state) { |
| Vector x = Vector::Random(kVectorSize); |
| Vector y = Vector(kVectorSize); |
| for (auto _ : state) { |
| y.block(0, 0, kVectorSize, 1) = x.block(0, 0, kVectorSize, 1); |
| } |
| CHECK_EQ((y - x).squaredNorm(), 0.); |
| } |
| BENCHMARK(Assign); |
| |
| static void AssignParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| Vector x = Vector::Random(kVectorSize); |
| Vector y = Vector(kVectorSize); |
| |
| for (auto _ : state) { |
| ParallelAssign(&context, num_threads, y, x); |
| } |
| CHECK_EQ((y - x).squaredNorm(), 0.); |
| } |
| BENCHMARK(AssignParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void D2X(benchmark::State& state) { |
| const Vector x = Vector::Random(kVectorSize); |
| const Vector D = Vector::Random(kVectorSize); |
| Vector y = Vector::Zero(kVectorSize); |
| for (auto _ : state) { |
| y = D.array().square() * x.array(); |
| } |
| CHECK_GT(y.squaredNorm(), 0.); |
| } |
| BENCHMARK(D2X); |
| |
| static void D2XParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| const Vector x = Vector::Random(kVectorSize); |
| const Vector D = Vector::Random(kVectorSize); |
| Vector y = Vector(kVectorSize); |
| |
| for (auto _ : state) { |
| ParallelAssign(&context, num_threads, y, D.array().square() * x.array()); |
| } |
| CHECK_GT(y.squaredNorm(), 0.); |
| } |
| BENCHMARK(D2XParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void DivideSqrt(benchmark::State& state) { |
| Vector diagonal = Vector::Random(kVectorSize).array().abs(); |
| const double radius = 0.5; |
| for (auto _ : state) { |
| diagonal = (diagonal / radius).array().sqrt(); |
| } |
| CHECK_GT(diagonal.squaredNorm(), 0.); |
| } |
| BENCHMARK(DivideSqrt); |
| |
| static void DivideSqrtParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| Vector diagonal = Vector::Random(kVectorSize).array().abs(); |
| const double radius = 0.5; |
| for (auto _ : state) { |
| ParallelAssign( |
| &context, num_threads, diagonal, (diagonal / radius).cwiseSqrt()); |
| } |
| CHECK_GT(diagonal.squaredNorm(), 0.); |
| } |
| BENCHMARK(DivideSqrtParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void Clamp(benchmark::State& state) { |
| Vector diagonal = Vector::Random(kVectorSize); |
| const double min = -0.5; |
| const double max = 0.5; |
| for (auto _ : state) { |
| for (int i = 0; i < kVectorSize; ++i) { |
| diagonal[i] = std::min(std::max(diagonal[i], min), max); |
| } |
| } |
| CHECK_LE(diagonal.maxCoeff(), 0.5); |
| CHECK_GE(diagonal.minCoeff(), -0.5); |
| } |
| BENCHMARK(Clamp); |
| |
| static void ClampParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| Vector diagonal = Vector::Random(kVectorSize); |
| const double min = -0.5; |
| const double max = 0.5; |
| for (auto _ : state) { |
| ParallelAssign( |
| &context, num_threads, diagonal, diagonal.array().max(min).min(max)); |
| } |
| CHECK_LE(diagonal.maxCoeff(), 0.5); |
| CHECK_GE(diagonal.minCoeff(), -0.5); |
| } |
| BENCHMARK(ClampParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void Norm(benchmark::State& state) { |
| const Vector x = Vector::Random(kVectorSize); |
| |
| double total = 0.; |
| for (auto _ : state) { |
| total += x.norm(); |
| } |
| CHECK_GT(total, 0.); |
| } |
| BENCHMARK(Norm); |
| |
| static void NormParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| const Vector x = Vector::Random(kVectorSize); |
| |
| double total = 0.; |
| for (auto _ : state) { |
| total += Norm(x, &context, num_threads); |
| } |
| CHECK_GT(total, 0.); |
| } |
| BENCHMARK(NormParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void Dot(benchmark::State& state) { |
| const Vector x = Vector::Random(kVectorSize); |
| const Vector y = Vector::Random(kVectorSize); |
| |
| double total = 0.; |
| for (auto _ : state) { |
| total += x.dot(y); |
| } |
| CHECK_NE(total, 0.); |
| } |
| BENCHMARK(Dot); |
| |
| static void DotParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| const Vector x = Vector::Random(kVectorSize); |
| const Vector y = Vector::Random(kVectorSize); |
| |
| double total = 0.; |
| for (auto _ : state) { |
| total += Dot(x, y, &context, num_threads); |
| } |
| CHECK_NE(total, 0.); |
| } |
| BENCHMARK(DotParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| static void Axpby(benchmark::State& state) { |
| const Vector x = Vector::Random(kVectorSize); |
| const Vector y = Vector::Random(kVectorSize); |
| Vector z = Vector::Zero(kVectorSize); |
| const double a = 3.1415; |
| const double b = 1.2345; |
| |
| for (auto _ : state) { |
| z = a * x + b * y; |
| } |
| CHECK_GT(z.squaredNorm(), 0.); |
| } |
| BENCHMARK(Axpby); |
| |
| static void AxpbyParallel(benchmark::State& state) { |
| const int num_threads = static_cast<int>(state.range(0)); |
| ContextImpl context; |
| context.EnsureMinimumThreads(num_threads); |
| |
| const Vector x = Vector::Random(kVectorSize); |
| const Vector y = Vector::Random(kVectorSize); |
| Vector z = Vector::Zero(kVectorSize); |
| const double a = 3.1415; |
| const double b = 1.2345; |
| |
| for (auto _ : state) { |
| Axpby(a, x, b, y, z, &context, num_threads); |
| } |
| CHECK_GT(z.squaredNorm(), 0.); |
| } |
| BENCHMARK(AxpbyParallel)->Arg(1)->Arg(2)->Arg(4)->Arg(8)->Arg(16); |
| |
| } // namespace ceres::internal |
| |
| BENCHMARK_MAIN(); |