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sparse_trisolver.cpp
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Thu, Feb 20, 21:55

sparse_trisolver.cpp
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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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