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https://gitlab.com/libeigen/eigen.git
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2840ac7e94
* renaming, e.g. LU ---> FullPivLU * split tests framework: more robust, e.g. dont generate empty tests if a number is skipped * make all remaining tests use that splitting, as needed. * Fix 4x4 inversion (see stable branch) * Transform::inverse() and geo_transform test : adapt to new inverse() API, it was also trying to instantiate inverse() for 3x4 matrices. * CMakeLists: more robust regexp to parse the version number * misc fixes in unit tests
133 lines
2.9 KiB
C++
133 lines
2.9 KiB
C++
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// g++ -I.. sparse_lu.cpp -O3 -g0 -I /usr/include/superlu/ -lsuperlu -lgfortran -DSIZE=1000 -DDENSITY=.05 && ./a.out
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#define EIGEN_SUPERLU_SUPPORT
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#define EIGEN_UMFPACK_SUPPORT
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#include <Eigen/Sparse>
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#define NOGMM
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#define NOMTL
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#ifndef SIZE
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#define SIZE 10
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#endif
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#ifndef DENSITY
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#define DENSITY 0.01
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#endif
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#ifndef REPEAT
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#define REPEAT 1
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#endif
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#include "BenchSparseUtil.h"
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#ifndef MINDENSITY
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#define MINDENSITY 0.0004
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#endif
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#ifndef NBTRIES
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#define NBTRIES 10
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#endif
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#define BENCH(X) \
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timer.reset(); \
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for (int _j=0; _j<NBTRIES; ++_j) { \
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timer.start(); \
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for (int _k=0; _k<REPEAT; ++_k) { \
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X \
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} timer.stop(); }
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typedef Matrix<Scalar,Dynamic,1> VectorX;
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#include <Eigen/LU>
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template<int Backend>
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void doEigen(const char* name, const EigenSparseMatrix& sm1, const VectorX& b, VectorX& x, int flags = 0)
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{
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std::cout << name << "..." << std::flush;
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BenchTimer timer; timer.start();
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SparseLU<EigenSparseMatrix,Backend> lu(sm1, flags);
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timer.stop();
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if (lu.succeeded())
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std::cout << ":\t" << timer.value() << endl;
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else
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{
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std::cout << ":\t FAILED" << endl;
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return;
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}
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bool ok;
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timer.reset(); timer.start();
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ok = lu.solve(b,&x);
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timer.stop();
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if (ok)
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std::cout << " solve:\t" << timer.value() << endl;
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else
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std::cout << " solve:\t" << " FAILED" << endl;
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//std::cout << x.transpose() << "\n";
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}
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int main(int argc, char *argv[])
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{
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int rows = SIZE;
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int cols = SIZE;
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float density = DENSITY;
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BenchTimer timer;
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VectorX b = VectorX::Random(cols);
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VectorX x = VectorX::Random(cols);
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bool densedone = false;
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//for (float density = DENSITY; density>=MINDENSITY; density*=0.5)
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// float density = 0.5;
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{
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EigenSparseMatrix sm1(rows, cols);
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fillMatrix(density, rows, cols, sm1);
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// dense matrices
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#ifdef DENSEMATRIX
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if (!densedone)
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{
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densedone = true;
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std::cout << "Eigen Dense\t" << density*100 << "%\n";
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DenseMatrix m1(rows,cols);
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eiToDense(sm1, m1);
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BenchTimer timer;
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timer.start();
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FullPivLU<DenseMatrix> lu(m1);
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timer.stop();
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std::cout << "Eigen/dense:\t" << timer.value() << endl;
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timer.reset();
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timer.start();
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lu.solve(b,&x);
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timer.stop();
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std::cout << " solve:\t" << timer.value() << endl;
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// std::cout << b.transpose() << "\n";
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// std::cout << x.transpose() << "\n";
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}
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#endif
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#ifdef EIGEN_UMFPACK_SUPPORT
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x.setZero();
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doEigen<Eigen::UmfPack>("Eigen/UmfPack (auto)", sm1, b, x, 0);
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#endif
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#ifdef EIGEN_SUPERLU_SUPPORT
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x.setZero();
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doEigen<Eigen::SuperLU>("Eigen/SuperLU (nat)", sm1, b, x, Eigen::NaturalOrdering);
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// doEigen<Eigen::SuperLU>("Eigen/SuperLU (MD AT+A)", sm1, b, x, Eigen::MinimumDegree_AT_PLUS_A);
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// doEigen<Eigen::SuperLU>("Eigen/SuperLU (MD ATA)", sm1, b, x, Eigen::MinimumDegree_ATA);
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doEigen<Eigen::SuperLU>("Eigen/SuperLU (COLAMD)", sm1, b, x, Eigen::ColApproxMinimumDegree);
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#endif
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}
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return 0;
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}
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