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			138 lines
		
	
	
		
			3.2 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			138 lines
		
	
	
		
			3.2 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| 
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| // g++ -I.. sparse_lu.cpp -O3 -g0 -I /usr/include/superlu/ -lsuperlu -lgfortran
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| // -DSIZE=1000 -DDENSITY=.05 && ./a.out
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| 
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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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| 
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| #define NOGMM
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| #define NOMTL
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| 
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| #ifndef SIZE
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| #define SIZE 10
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| #endif
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| 
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| #ifndef DENSITY
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| #define DENSITY 0.01
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| #endif
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| 
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| #ifndef REPEAT
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| #define REPEAT 1
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| #endif
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| 
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| #include "BenchSparseUtil.h"
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| 
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| #ifndef MINDENSITY
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| #define MINDENSITY 0.0004
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| #endif
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| 
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| #ifndef NBTRIES
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| #define NBTRIES 10
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| #endif
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| 
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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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|     }                                     \
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|     timer.stop();                         \
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|   }
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| 
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| typedef Matrix<Scalar, Dynamic, 1> VectorX;
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| 
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| #include <Eigen/LU>
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| 
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| template <int Backend>
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| void doEigen(const char* name, const EigenSparseMatrix& sm1, const VectorX& b,
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|              VectorX& x, int flags = 0) {
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|   std::cout << name << "..." << std::flush;
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|   BenchTimer timer;
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|   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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|     std::cout << ":\t FAILED" << endl;
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|     return;
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|   }
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| 
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|   bool ok;
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|   timer.reset();
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|   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"
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|               << " FAILED" << endl;
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| 
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|   // std::cout << x.transpose() << "\n";
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| }
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| 
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| int main(int argc, char* argv[]) {
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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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| 
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|   VectorX b = VectorX::Random(cols);
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|   VectorX x = VectorX::Random(cols);
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| 
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|   bool densedone = false;
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| 
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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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| 
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| // dense matrices
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| #ifdef DENSEMATRIX
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|     if (!densedone) {
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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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| 
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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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| 
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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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| 
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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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| 
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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,
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|                             Eigen::NaturalOrdering);
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|     //     doEigen<Eigen::SuperLU>("Eigen/SuperLU (MD AT+A)", sm1, b, x,
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|     //     Eigen::MinimumDegree_AT_PLUS_A);
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|     //     doEigen<Eigen::SuperLU>("Eigen/SuperLU (MD ATA)", sm1, b, x,
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|     //     Eigen::MinimumDegree_ATA);
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|     doEigen<Eigen::SuperLU>("Eigen/SuperLU (COLAMD)", sm1, b, x,
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|                             Eigen::ColApproxMinimumDegree);
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| #endif
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|   }
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| 
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|   return 0;
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| }
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