internal/ceres/parallel_vector_operations_benchmark.cc - ceres-solver - Git at Google

 // 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.

 #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();
	// 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.

	#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();