mirror of
https://gitlab.com/libeigen/eigen.git
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173 lines
6.4 KiB
C++
173 lines
6.4 KiB
C++
#include <iostream>
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#include "BenchTimer.h"
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#include <Eigen/Dense>
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#include <map>
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#include <vector>
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#include <string>
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#include <sstream>
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using namespace Eigen;
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std::map<std::string, Array<float, 1, 8, DontAlign | RowMajor> > results;
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std::vector<std::string> labels;
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std::vector<Array2i> sizes;
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template <typename Solver, typename MatrixType>
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EIGEN_DONT_INLINE void compute_norm_equation(Solver &solver, const MatrixType &A) {
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if (A.rows() != A.cols())
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solver.compute(A.transpose() * A);
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else
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solver.compute(A);
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}
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template <typename Solver, typename MatrixType>
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EIGEN_DONT_INLINE void compute(Solver &solver, const MatrixType &A) {
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solver.compute(A);
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}
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template <typename Scalar, int Size>
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void bench(int id, int rows, int size = Size) {
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typedef Matrix<Scalar, Dynamic, Size> Mat;
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typedef Matrix<Scalar, Dynamic, Dynamic> MatDyn;
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typedef Matrix<Scalar, Size, Size> MatSquare;
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Mat A(rows, size);
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A.setRandom();
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if (rows == size) A = A * A.adjoint();
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BenchTimer t_llt, t_ldlt, t_lu, t_fplu, t_qr, t_cpqr, t_cod, t_fpqr, t_jsvd, t_bdcsvd;
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int tries = 5;
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int rep = 1000 / size;
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if (rep == 0) rep = 1;
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// rep = rep*rep;
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LLT<MatSquare> llt(size);
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LDLT<MatSquare> ldlt(size);
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PartialPivLU<MatSquare> lu(size);
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FullPivLU<MatSquare> fplu(size, size);
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HouseholderQR<Mat> qr(A.rows(), A.cols());
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ColPivHouseholderQR<Mat> cpqr(A.rows(), A.cols());
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CompleteOrthogonalDecomposition<Mat> cod(A.rows(), A.cols());
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FullPivHouseholderQR<Mat> fpqr(A.rows(), A.cols());
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JacobiSVD<MatDyn, ComputeThinU | ComputeThinV> jsvd(A.rows(), A.cols());
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BDCSVD<MatDyn, ComputeThinU | ComputeThinV> bdcsvd(A.rows(), A.cols());
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BENCH(t_llt, tries, rep, compute_norm_equation(llt, A));
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BENCH(t_ldlt, tries, rep, compute_norm_equation(ldlt, A));
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BENCH(t_lu, tries, rep, compute_norm_equation(lu, A));
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if (size <= 1000) BENCH(t_fplu, tries, rep, compute_norm_equation(fplu, A));
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BENCH(t_qr, tries, rep, compute(qr, A));
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BENCH(t_cpqr, tries, rep, compute(cpqr, A));
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BENCH(t_cod, tries, rep, compute(cod, A));
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if (size * rows <= 10000000) BENCH(t_fpqr, tries, rep, compute(fpqr, A));
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if (size < 500) // JacobiSVD is really too slow for too large matrices
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BENCH(t_jsvd, tries, rep, jsvd.compute(A));
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// if(size*rows<=20000000)
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BENCH(t_bdcsvd, tries, rep, bdcsvd.compute(A));
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results["LLT"][id] = t_llt.best();
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results["LDLT"][id] = t_ldlt.best();
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results["PartialPivLU"][id] = t_lu.best();
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results["FullPivLU"][id] = t_fplu.best();
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results["HouseholderQR"][id] = t_qr.best();
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results["ColPivHouseholderQR"][id] = t_cpqr.best();
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results["CompleteOrthogonalDecomposition"][id] = t_cod.best();
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results["FullPivHouseholderQR"][id] = t_fpqr.best();
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results["JacobiSVD"][id] = t_jsvd.best();
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results["BDCSVD"][id] = t_bdcsvd.best();
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}
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int main() {
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labels.push_back("LLT");
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labels.push_back("LDLT");
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labels.push_back("PartialPivLU");
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labels.push_back("FullPivLU");
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labels.push_back("HouseholderQR");
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labels.push_back("ColPivHouseholderQR");
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labels.push_back("CompleteOrthogonalDecomposition");
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labels.push_back("FullPivHouseholderQR");
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labels.push_back("JacobiSVD");
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labels.push_back("BDCSVD");
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for (int i = 0; i < labels.size(); ++i) results[labels[i]].fill(-1);
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const int small = 8;
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sizes.push_back(Array2i(small, small));
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sizes.push_back(Array2i(100, 100));
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sizes.push_back(Array2i(1000, 1000));
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sizes.push_back(Array2i(4000, 4000));
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sizes.push_back(Array2i(10000, small));
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sizes.push_back(Array2i(10000, 100));
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sizes.push_back(Array2i(10000, 1000));
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sizes.push_back(Array2i(10000, 4000));
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using namespace std;
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for (int k = 0; k < sizes.size(); ++k) {
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cout << sizes[k](0) << "x" << sizes[k](1) << "...\n";
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bench<float, Dynamic>(k, sizes[k](0), sizes[k](1));
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}
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cout.width(32);
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cout << "solver/size";
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cout << " ";
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for (int k = 0; k < sizes.size(); ++k) {
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std::stringstream ss;
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ss << sizes[k](0) << "x" << sizes[k](1);
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cout.width(10);
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cout << ss.str();
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cout << " ";
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}
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cout << endl;
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for (int i = 0; i < labels.size(); ++i) {
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cout.width(32);
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cout << labels[i];
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cout << " ";
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ArrayXf r = (results[labels[i]] * 100000.f).floor() / 100.f;
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for (int k = 0; k < sizes.size(); ++k) {
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cout.width(10);
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if (r(k) >= 1e6)
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cout << "-";
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else
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cout << r(k);
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cout << " ";
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}
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cout << endl;
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}
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// HTML output
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cout << "<table class=\"manual\">" << endl;
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cout << "<tr><th>solver/size</th>" << endl;
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for (int k = 0; k < sizes.size(); ++k) cout << " <th>" << sizes[k](0) << "x" << sizes[k](1) << "</th>";
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cout << "</tr>" << endl;
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for (int i = 0; i < labels.size(); ++i) {
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cout << "<tr";
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if (i % 2 == 1) cout << " class=\"alt\"";
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cout << "><td>" << labels[i] << "</td>";
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ArrayXf r = (results[labels[i]] * 100000.f).floor() / 100.f;
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for (int k = 0; k < sizes.size(); ++k) {
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if (r(k) >= 1e6)
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cout << "<td>-</td>";
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else {
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cout << "<td>" << r(k);
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if (i > 0) cout << " (x" << numext::round(10.f * results[labels[i]](k) / results["LLT"](k)) / 10.f << ")";
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if (i < 4 && sizes[k](0) != sizes[k](1)) cout << " <sup><a href=\"#note_ls\">*</a></sup>";
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cout << "</td>";
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}
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}
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cout << "</tr>" << endl;
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}
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cout << "</table>" << endl;
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// cout << "LLT (ms) " << (results["LLT"]*1000.).format(fmt) << "\n";
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// cout << "LDLT (%) " << (results["LDLT"]/results["LLT"]).format(fmt) << "\n";
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// cout << "PartialPivLU (%) " << (results["PartialPivLU"]/results["LLT"]).format(fmt) << "\n";
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// cout << "FullPivLU (%) " << (results["FullPivLU"]/results["LLT"]).format(fmt) << "\n";
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// cout << "HouseholderQR (%) " << (results["HouseholderQR"]/results["LLT"]).format(fmt) <<
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// "\n"; cout << "ColPivHouseholderQR (%) " <<
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// (results["ColPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n"; cout << "CompleteOrthogonalDecomposition (%)
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// " << (results["CompleteOrthogonalDecomposition"]/results["LLT"]).format(fmt) << "\n"; cout <<
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// "FullPivHouseholderQR (%) " << (results["FullPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n";
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// cout << "JacobiSVD (%) " << (results["JacobiSVD"]/results["LLT"]).format(fmt) << "\n";
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// cout << "BDCSVD (%) " << (results["BDCSVD"]/results["LLT"]).format(fmt) << "\n";
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}
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