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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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// POSSIBILITY OF SUCH DAMAGE.
#include <algorithm>
#include "absl/log/check.h"
#include "benchmark/benchmark.h"
#include "ceres/eigen_vector_ops.h"
#include "ceres/parallel_for.h"
namespace ceres::internal {
// Older versions of benchmark library (for example, one shipped with
// ubuntu 20.04) do not support range generation and range products
#define VECTOR_SIZES(num_threads) \
Args({1 << 7, num_threads}) \
->Args({1 << 8, num_threads}) \
->Args({1 << 9, num_threads}) \
->Args({1 << 10, num_threads}) \
->Args({1 << 11, num_threads}) \
->Args({1 << 12, num_threads}) \
->Args({1 << 13, num_threads}) \
->Args({1 << 14, num_threads}) \
->Args({1 << 15, num_threads}) \
->Args({1 << 16, num_threads}) \
->Args({1 << 17, num_threads}) \
->Args({1 << 18, num_threads}) \
->Args({1 << 19, num_threads}) \
->Args({1 << 20, num_threads}) \
->Args({1 << 21, num_threads}) \
->Args({1 << 22, num_threads}) \
->Args({1 << 23, num_threads})
#define VECTOR_SIZE_THREADS \
VECTOR_SIZES(1) \
->VECTOR_SIZES(2) \
->VECTOR_SIZES(4) \
->VECTOR_SIZES(8) \
->VECTOR_SIZES(16)
static void SetZero(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
Vector x = Vector::Random(kVectorSize);
for (auto _ : state) {
x.setZero();
}
CHECK_EQ(x.squaredNorm(), 0.);
}
BENCHMARK(SetZero)->VECTOR_SIZES(1);
static void SetZeroParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void Negate(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
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)->VECTOR_SIZES(1);
static void NegateParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void Assign(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
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)->VECTOR_SIZES(1);
static void AssignParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void D2X(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
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)->VECTOR_SIZES(1);
static void D2XParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void DivideSqrt(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
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)->VECTOR_SIZES(1);
static void DivideSqrtParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void Clamp(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
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)->VECTOR_SIZES(1);
static void ClampParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void Norm(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const Vector x = Vector::Random(kVectorSize);
double total = 0.;
for (auto _ : state) {
total += x.norm();
}
CHECK_GT(total, 0.);
}
BENCHMARK(Norm)->VECTOR_SIZES(1);
static void NormParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void Dot(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
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)->VECTOR_SIZES(1);
static void DotParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
static void Axpby(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
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)->VECTOR_SIZES(1);
static void AxpbyParallel(benchmark::State& state) {
const int kVectorSize = static_cast<int>(state.range(0));
const int num_threads = static_cast<int>(state.range(1));
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)->VECTOR_SIZE_THREADS;
} // namespace ceres::internal
BENCHMARK_MAIN();