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							132 lines
						
					
					
						
							2.9 KiB
						
					
					
				
			
		
		
		
			
			
			
				
					
				
				
					
				
			
		
		
	
	
							132 lines
						
					
					
						
							2.9 KiB
						
					
					
				
								
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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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