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//g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 && ./a.out
//g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
// -DNOGMM -DNOMTL
// -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
#ifndef SIZE
#define SIZE 10000
#endif
#ifndef DENSITY
#define DENSITY 0.01
#endif
#ifndef REPEAT
#define REPEAT 1
#endif
#include "BenchSparseUtil.h"
#ifndef MINDENSITY
#define MINDENSITY 0.0004
#endif
#ifndef NBTRIES
#define NBTRIES 10
#endif
#define BENCH(X) \
timer.reset(); \ for (int _j=0; _j<NBTRIES; ++_j) { \ timer.start(); \ for (int _k=0; _k<REPEAT; ++_k) { \ X \ } timer.stop(); }
typedef SparseMatrix<Scalar,UpperTriangular> EigenSparseTriMatrix; typedef SparseMatrix<Scalar,RowMajorBit|UpperTriangular> EigenSparseTriMatrixRow;
void fillMatrix(float density, int rows, int cols, EigenSparseTriMatrix& dst) { dst.startFill(rows*cols*density); for(int j = 0; j < cols; j++) { for(int i = 0; i < j; i++) { Scalar v = (internal::random<float>(0,1) < density) ? internal::random<Scalar>() : 0; if (v!=0) dst.fill(i,j) = v; } dst.fill(j,j) = internal::random<Scalar>(); } dst.endFill(); }
int main(int argc, char *argv[]) { int rows = SIZE; int cols = SIZE; float density = DENSITY; BenchTimer timer; #if 1
EigenSparseTriMatrix sm1(rows,cols); typedef Matrix<Scalar,Dynamic,1> DenseVector; DenseVector b = DenseVector::Random(cols); DenseVector x = DenseVector::Random(cols);
bool densedone = false;
for (float density = DENSITY; density>=MINDENSITY; density*=0.5) { EigenSparseTriMatrix sm1(rows, cols); fillMatrix(density, rows, cols, sm1);
// dense matrices
#ifdef DENSEMATRIX
if (!densedone) { densedone = true; std::cout << "Eigen Dense\t" << density*100 << "%\n"; DenseMatrix m1(rows,cols); Matrix<Scalar,Dynamic,Dynamic,Dynamic,Dynamic,RowMajorBit> m2(rows,cols); eiToDense(sm1, m1); m2 = m1;
BENCH(x = m1.marked<UpperTriangular>().solveTriangular(b);) std::cout << " colmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << x.transpose() << "\n";
BENCH(x = m2.marked<UpperTriangular>().solveTriangular(b);) std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << x.transpose() << "\n";
} #endif
// eigen sparse matrices
{ std::cout << "Eigen sparse\t" << density*100 << "%\n"; EigenSparseTriMatrixRow sm2 = sm1;
BENCH(x = sm1.solveTriangular(b);) std::cout << " colmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << x.transpose() << "\n";
BENCH(x = sm2.solveTriangular(b);) std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << x.transpose() << "\n";
// x = b;
// BENCH(sm1.inverseProductInPlace(x);)
// std::cout << " colmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl;
// std::cerr << x.transpose() << "\n";
//
// x = b;
// BENCH(sm2.inverseProductInPlace(x);)
// std::cout << " rowmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl;
// std::cerr << x.transpose() << "\n";
}
// CSparse
#ifdef CSPARSE
{ std::cout << "CSparse \t" << density*100 << "%\n"; cs *m1; eiToCSparse(sm1, m1);
BENCH(x = b; if (!cs_lsolve (m1, x.data())){std::cerr << "cs_lsolve failed\n"; break;}; ) std::cout << " colmajor^-1 * b:\t" << timer.value() << endl; } #endif
// GMM++
#ifndef NOGMM
{ std::cout << "GMM++ sparse\t" << density*100 << "%\n"; GmmSparse m1(rows,cols); gmm::csr_matrix<Scalar> m2; eiToGmm(sm1, m1); gmm::copy(m1,m2); std::vector<Scalar> gmmX(cols), gmmB(cols); Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols) = x; Map<Matrix<Scalar,Dynamic,1> >(&gmmB[0], cols) = b;
gmmX = gmmB; BENCH(gmm::upper_tri_solve(m1, gmmX, false);) std::cout << " colmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n";
gmmX = gmmB; BENCH(gmm::upper_tri_solve(m2, gmmX, false);) timer.stop(); std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n";
} #endif
// MTL4
#ifndef NOMTL
{ std::cout << "MTL4\t" << density*100 << "%\n"; MtlSparse m1(rows,cols); MtlSparseRowMajor m2(rows,cols); eiToMtl(sm1, m1); m2 = m1; mtl::dense_vector<Scalar> x(rows, 1.0); mtl::dense_vector<Scalar> b(rows, 1.0);
BENCH(x = mtl::upper_trisolve(m1,b);) std::cout << " colmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << x << "\n";
BENCH(x = mtl::upper_trisolve(m2,b);) std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl; // std::cerr << x << "\n";
} #endif
std::cout << "\n\n"; } #endif
#if 0
// bench small matrices (in-place versus return bye value)
{ timer.reset(); for (int _j=0; _j<10; ++_j) { Matrix4f m = Matrix4f::Random(); Vector4f b = Vector4f::Random(); Vector4f x = Vector4f::Random(); timer.start(); for (int _k=0; _k<1000000; ++_k) { b = m.inverseProduct(b); } timer.stop(); } std::cout << "4x4 :\t" << timer.value() << endl; }
{ timer.reset(); for (int _j=0; _j<10; ++_j) { Matrix4f m = Matrix4f::Random(); Vector4f b = Vector4f::Random(); Vector4f x = Vector4f::Random(); timer.start(); for (int _k=0; _k<1000000; ++_k) { m.inverseProductInPlace(x); } timer.stop(); } std::cout << "4x4 IP :\t" << timer.value() << endl; } #endif
return 0; }
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