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Update benchmark for dense solver to stress least-squares pb, and to output a HTML table
This commit is contained in:
Gael Guennebaud
parent
72950effdf
commit
9b76be9d21
@ -2,47 +2,74 @@
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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,1,4> > results;
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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
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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
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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 size = Size)
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void bench(int id, int rows, int size = Size)
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{
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typedef Matrix<Scalar,Size,Size> Mat;
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Mat A(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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A = A*A.adjoint();
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if(rows==size)
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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 svd_opt = ComputeThinU|ComputeThinV;
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int tries = 3;
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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<Mat> llt(A);
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LDLT<Mat> ldlt(A);
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PartialPivLU<Mat> lu(A);
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FullPivLU<Mat> fplu(A);
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HouseholderQR<Mat> qr(A);
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ColPivHouseholderQR<Mat> cpqr(A);
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CompleteOrthogonalDecomposition<Mat> cod(A);
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FullPivHouseholderQR<Mat> fpqr(A);
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JacobiSVD<Mat> jsvd(A.rows(),A.cols());
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BDCSVD<Mat> bdcsvd(A.rows(),A.cols());
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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> jsvd(A.rows(),A.cols());
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BDCSVD<MatDyn> bdcsvd(A.rows(),A.cols());
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BENCH(t_llt, tries, rep, llt.compute(A));
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BENCH(t_ldlt, tries, rep, ldlt.compute(A));
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BENCH(t_lu, tries, rep, lu.compute(A));
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BENCH(t_fplu, tries, rep, fplu.compute(A));
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BENCH(t_qr, tries, rep, qr.compute(A));
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BENCH(t_cpqr, tries, rep, cpqr.compute(A));
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BENCH(t_cod, tries, rep, cod.compute(A));
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BENCH(t_fpqr, tries, rep, fpqr.compute(A));
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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)
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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)
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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,ComputeFullU|ComputeFullV));
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BENCH(t_bdcsvd, tries, rep, bdcsvd.compute(A,ComputeFullU|ComputeFullV));
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BENCH(t_jsvd, tries, rep, jsvd.compute(A,svd_opt));
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// if(size*rows<=20000000)
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BENCH(t_bdcsvd, tries, rep, bdcsvd.compute(A,svd_opt));
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results["LLT"][id] = t_llt.best();
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results["LDLT"][id] = t_ldlt.best();
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@ -52,33 +79,108 @@ void bench(int id, int size = Size)
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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] = size<500 ? t_jsvd.best() : 0;
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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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{
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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)
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results[labels[i]].fill(-1);
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const int small = 8;
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const int medium = 100;
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const int large = 1000;
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const int xl = 4000;
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bench<float,small>(0);
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bench<float,Dynamic>(1,medium);
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bench<float,Dynamic>(2,large);
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bench<float,Dynamic>(3,xl);
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IOFormat fmt(3, 0, " \t", "\n", "", "");
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std::cout << "solver/size " << small << "\t" << medium << "\t" << large << "\t" << xl << "\n";
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std::cout << "LLT (ms) " << (results["LLT"]/1000.).format(fmt) << "\n";
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std::cout << "LDLT (%) " << (results["LDLT"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "PartialPivLU (%) " << (results["PartialPivLU"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "FullPivLU (%) " << (results["FullPivLU"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "HouseholderQR (%) " << (results["HouseholderQR"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "ColPivHouseholderQR (%) " << (results["ColPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "CompleteOrthogonalDecomposition (%) " << (results["CompleteOrthogonalDecomposition"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "FullPivHouseholderQR (%) " << (results["FullPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "JacobiSVD (%) " << (results["JacobiSVD"]/results["LLT"]).format(fmt) << "\n";
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std::cout << "BDCSVD (%) " << (results["BDCSVD"]/results["LLT"]).format(fmt) << "\n";
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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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{
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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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{
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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); cout << ss.str(); 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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{
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cout.width(32); cout << labels[i]; 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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{
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cout.width(10);
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if(r(k)>=1e6) cout << "-";
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else 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)
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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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{
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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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{
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if(r(k)>=1e6) cout << "<td>-</td>";
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else
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{
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cout << "<td>" << r(k);
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if(i>0)
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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))
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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) << "\n";
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// cout << "ColPivHouseholderQR (%) " << (results["ColPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n";
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// cout << "CompleteOrthogonalDecomposition (%) " << (results["CompleteOrthogonalDecomposition"]/results["LLT"]).format(fmt) << "\n";
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// cout << "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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