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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include <cmath>
#include <cstdio>
#include <thread>
#ifdef _OPENMP
#include <omp.h>
#endif
#include <Eigen/Dense>
#include <Eigen/SVD>
#include "open3d/Open3D.h"
#define NUM_THREADS 4
#define NUM_START 1
#define NUM_END 10
using namespace open3d;
void simple_task() {
int n_a_rows = 2000;
int n_a_cols = 2000;
int n_b_rows = 2000;
int n_b_cols = 2000;
Eigen::MatrixXd a(n_a_rows, n_a_cols);
for (int i = 0; i < n_a_rows; ++i)
for (int j = 0; j < n_a_cols; ++j) a(i, j) = n_a_cols * i + j;
Eigen::MatrixXd b(n_b_rows, n_b_cols);
for (int i = 0; i < n_b_rows; ++i)
for (int j = 0; j < n_b_cols; ++j) b(i, j) = n_b_cols * i + j;
Eigen::MatrixXd d(n_a_rows, n_b_cols);
d = a * b;
}
void svd_task() {
int n_a_rows = 10000;
int n_a_cols = 200;
Eigen::MatrixXd a(n_a_rows, n_a_cols);
for (int i = 0; i < n_a_rows; ++i)
for (int j = 0; j < n_a_cols; ++j) a(i, j) = n_a_cols * i + j;
Eigen::JacobiSVD<Eigen::MatrixXd> svd(
a, Eigen::ComputeThinU | Eigen::ComputeThinV);
Eigen::MatrixXd pca = svd.matrixU().block<10000, 10>(0, 0).transpose() * a;
}
void TestMatrixMultiplication(int argc, char **argv) {
int i = 0, nSum = 0, nStart = NUM_START, nEnd = NUM_END;
int nThreads = 1, nTmp = nStart + nEnd;
unsigned uTmp = (unsigned(nEnd - nStart + 1) * unsigned(nTmp)) / 2;
int nSumCalc = uTmp;
if (nTmp < 0) {
nSumCalc = -nSumCalc;
}
#ifdef _OPENMP
utility::LogInfo("OpenMP is supported.");
#else
utility::LogInfo("OpenMP is not supported.");
#endif
#ifdef _OPENMP
omp_set_num_threads(NUM_THREADS);
#endif
#pragma omp parallel default(none) private(i) \
shared(nSum, nThreads, nStart, nEnd)
{
#ifdef _OPENMP
#pragma omp master
nThreads = omp_get_num_threads();
#endif
#pragma omp for
for (i = nStart; i <= nEnd; ++i) {
#pragma omp atomic
nSum += i;
}
}
if (nThreads == NUM_THREADS) {
utility::LogInfo("{:d} OpenMP threads were used.", NUM_THREADS);
} else {
utility::LogInfo("Expected {:d} OpenMP threads, but {:d} were used.",
NUM_THREADS, nThreads);
}
if (nSum != nSumCalc) {
utility::LogInfo(
"The sum of {:d} through {:d} should be {:d}, "
"but {:d} was reported!",
NUM_START, NUM_END, nSumCalc, nSum);
} else {
utility::LogInfo("The sum of {:d} through {:d} is {:d}", NUM_START,
NUM_END, nSum);
}
int test_thread = 256;
if (argc > 1) {
test_thread = std::stoi(argv[1]);
}
open3d::utility::LogInfo("Benchmark multithreading up to {:d} threads.",
test_thread);
for (int i = 1; i <= test_thread; i *= 2) {
std::string buff =
fmt::format("simple task, {:d} tasks, {:d} threads", i, i);
open3d::utility::ScopeTimer t(buff.c_str());
#ifdef _OPENMP
omp_set_num_threads(i);
#endif
#pragma omp parallel default(none) shared(nThreads)
{ simple_task(); }
}
for (int i = 1; i <= test_thread; i *= 2) {
std::string buff =
fmt::format("simple task, {:d} tasks, {:d} threads", i, i);
open3d::utility::ScopeTimer t(buff.c_str());
std::vector<std::thread> threads(i);
for (int k = 0; k < i; k++) {
threads[k] = std::thread(simple_task);
}
for (int k = 0; k < i; k++) {
threads[k].join();
}
}
for (int i = 1; i <= test_thread; i *= 2) {
std::string buff = fmt::format("svd, {:d} tasks, {:d} threads", i, i);
open3d::utility::ScopeTimer t(buff.c_str());
#ifdef _OPENMP
omp_set_num_threads(i);
#endif
#pragma omp parallel default(none) shared(nThreads)
{ svd_task(); }
}
for (int i = 1; i <= test_thread; i *= 2) {
std::string buff =
fmt::format("svd task, {:d} tasks, {:d} threads", i, i);
open3d::utility::ScopeTimer t(buff.c_str());
std::vector<std::thread> threads(i);
for (int k = 0; k < i; k++) {
threads[k] = std::thread(svd_task);
}
for (int k = 0; k < i; k++) {
threads[k].join();
}
}
}
inline void ComputeSomething(int i,
Eigen::Vector6d &A_r,
double &r,
std::vector<Eigen::Vector3d> &data) {
const Eigen::Vector3d &vs = data[i];
const Eigen::Vector3d &vt = data[i];
const Eigen::Vector3d &nt = data[i];
r = (vs - vt).dot(nt);
// A_r.setZero();
A_r.block<3, 1>(0, 0).noalias() = vs.cross(nt);
A_r.block<3, 1>(3, 0).noalias() = nt;
}
/// Function to simulate building Jacobian matrix
/// uses simple way of using OpenMP and std::bind
void TestBindedFunction() {
// data generation
const int NCORR = 200000000;
std::vector<Eigen::Vector3d> data;
{
open3d::utility::ScopeTimer timer1("Data generation");
data.resize(NCORR);
#pragma omp for nowait
for (int i = 0; i < NCORR; i++) {
data[i] = Eigen::Vector3d::Random();
}
}
// data we want to build
Eigen::Matrix6d ATA;
Eigen::Vector6d ATb;
// to do using private ATA
// https://stackoverflow.com/questions/24948395/openmp-calling-global-variables-through-functions
auto f = std::bind(ComputeSomething, std::placeholders::_1,
std::placeholders::_2, std::placeholders::_3, data);
auto f_lambda = [&](int i, Eigen::Vector6d &A_r, double &r) {
ComputeSomething(i, A_r, r, data);
};
ATA.setZero();
ATb.setZero();
{
open3d::utility::ScopeTimer timer("Calling binding function");
#pragma omp parallel
{
Eigen::Matrix6d ATA_private;
Eigen::Vector6d ATb_private;
ATA_private.setZero();
ATb_private.setZero();
#pragma omp for nowait
for (int i = 0; i < NCORR; i++) {
Eigen::Vector6d A_r;
double r;
f(i, A_r, r);
ATA_private.noalias() += A_r * A_r.transpose();
ATb_private.noalias() += A_r * r;
}
#pragma omp critical
{
ATA += ATA_private;
ATb += ATb_private;
} // omp critical
} // omp parallel
}
std::cout << ATA << std::endl;
std::cout << ATb << std::endl;
ATA.setZero();
ATb.setZero();
{
open3d::utility::ScopeTimer timer("Calling lambda function");
#pragma omp parallel
{
Eigen::Matrix6d ATA_private;
Eigen::Vector6d ATb_private;
ATA_private.setZero();
ATb_private.setZero();
#pragma omp for nowait
for (int i = 0; i < NCORR; i++) {
Eigen::Vector6d A_r;
double r;
f_lambda(i, A_r, r);
ATA_private.noalias() += A_r * A_r.transpose();
ATb_private.noalias() += A_r * r;
}
#pragma omp critical
{
ATA += ATA_private;
ATb += ATb_private;
} // omp critical
} // omp parallel
}
std::cout << ATA << std::endl;
std::cout << ATb << std::endl;
ATA.setZero();
ATb.setZero();
{
open3d::utility::ScopeTimer timer("Calling function directly");
#pragma omp parallel
{
Eigen::Matrix6d ATA_private;
Eigen::Vector6d ATb_private;
ATA_private.setZero();
ATb_private.setZero();
#pragma omp for nowait
for (int i = 0; i < NCORR; i++) {
Eigen::Vector6d A_r;
double r;
ComputeSomething(i, A_r, r, data);
ATA_private.noalias() += A_r * A_r.transpose();
ATb_private.noalias() += A_r * r;
}
#pragma omp critical
{
ATA += ATA_private;
ATb += ATb_private;
} // omp critical
} // omp parallel
}
std::cout << ATA << std::endl;
std::cout << ATb << std::endl;
ATA.setZero();
ATb.setZero();
{
open3d::utility::ScopeTimer timer("Direct optration");
#pragma omp parallel
{
Eigen::Matrix6d ATA_private;
Eigen::Vector6d ATb_private;
ATA_private.setZero();
ATb_private.setZero();
#pragma omp for nowait
for (int i = 0; i < NCORR; i++) {
const Eigen::Vector3d &vs = data[i];
const Eigen::Vector3d &vt = data[i];
const Eigen::Vector3d &nt = data[i];
double r = (vs - vt).dot(nt);
Eigen::Vector6d A_r;
A_r.block<3, 1>(0, 0).noalias() = vs.cross(nt);
A_r.block<3, 1>(3, 0).noalias() = nt;
ATA_private.noalias() += A_r * A_r.transpose();
ATb_private.noalias() += A_r * r;
}
#pragma omp critical
{
ATA += ATA_private;
ATb += ATb_private;
} // omp critical
} // omp parallel
}
std::cout << ATA << std::endl;
std::cout << ATb << std::endl;
}
int main(int argc, char **argv) {
using namespace open3d;
if (utility::ProgramOptionExists(argc, argv, "--test_bind")) {
TestBindedFunction();
} else {
TestMatrixMultiplication(argc, argv);
}
return 0;
}
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