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https://gitlab.com/libeigen/eigen.git
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* added an in-place version of inverseProduct which
might be twice faster fot small fixed size matrix * added a sparse triangular solver (sparse version of inverseProduct) * various other improvements in the Sparse module
This commit is contained in:
Gael Guennebaud
206
bench/sparse_trisolver.cpp
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206
bench/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
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//g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05 -DSIZE=2000 && ./a.out
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// -DNOGMM -DNOMTL
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#ifndef SIZE
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#define SIZE 10000
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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 SparseMatrix<Scalar,Upper> EigenSparseTriMatrix;
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typedef SparseMatrix<Scalar,RowMajorBit|Upper> EigenSparseTriMatrixRow;
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void fillMatrix(float density, int rows, int cols, EigenSparseTriMatrix& dst)
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{
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dst.startFill(rows*cols*density);
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for(int j = 0; j < cols; j++)
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{
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for(int i = 0; i < j; i++)
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{
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Scalar v = (ei_random<float>(0,1) < density) ? ei_random<Scalar>() : 0;
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if (v!=0)
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dst.fill(i,j) = v;
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}
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dst.fill(j,j) = ei_random<Scalar>();
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}
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dst.endFill();
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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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#if 1
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EigenSparseTriMatrix sm1(rows,cols);
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VectorXf b = VectorXf::random(cols);
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VectorXf x = VectorXf::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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{
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EigenSparseTriMatrix 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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Matrix<Scalar,Dynamic,Dynamic,Dynamic,Dynamic,RowMajorBit> m2(rows,cols);
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eiToDense(sm1, m1);
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m2 = m1;
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BENCH(x = m1.marked<Upper>().inverseProduct(b);)
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std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
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std::cerr << x.transpose() << "\n";
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BENCH(x = m2.marked<Upper>().inverseProduct(b);)
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std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
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std::cerr << x.transpose() << "\n";
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}
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#endif
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// eigen sparse matrices
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{
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std::cout << "Eigen sparse\t" << density*100 << "%\n";
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EigenSparseTriMatrixRow sm2 = sm1;
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BENCH(x = sm1.inverseProduct(b);)
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std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
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std::cerr << x.transpose() << "\n";
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BENCH(x = sm2.inverseProduct(b);)
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std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
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std::cerr << x.transpose() << "\n";
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// x = b;
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// BENCH(sm1.inverseProductInPlace(x);)
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// std::cout << " colmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl;
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// std::cerr << x.transpose() << "\n";
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//
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// x = b;
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// BENCH(sm2.inverseProductInPlace(x);)
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// std::cout << " rowmajor^-1 * b:\t" << timer.value() << " (inplace)" << endl;
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// std::cerr << x.transpose() << "\n";
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}
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// GMM++
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#ifndef NOGMM
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{
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std::cout << "GMM++ sparse\t" << density*100 << "%\n";
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GmmSparse m1(rows,cols);
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gmm::csr_matrix<Scalar> m2;
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eiToGmm(sm1, m1);
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gmm::copy(m1,m2);
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std::vector<Scalar> gmmX(cols), gmmB(cols);
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Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols) = x;
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Map<Matrix<Scalar,Dynamic,1> >(&gmmB[0], cols) = b;
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gmmX = gmmB;
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BENCH(gmm::upper_tri_solve(m1, gmmX, false);)
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std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
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std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n";
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gmmX = gmmB;
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BENCH(gmm::upper_tri_solve(m2, gmmX, false);)
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timer.stop();
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std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
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std::cerr << Map<Matrix<Scalar,Dynamic,1> >(&gmmX[0], cols).transpose() << "\n";
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}
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#endif
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// MTL4
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#ifndef NOMTL
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{
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std::cout << "MTL4\t" << density*100 << "%\n";
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MtlSparse m1(rows,cols);
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MtlSparseRowMajor m2(rows,cols);
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eiToMtl(sm1, m1);
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m2 = m1;
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mtl::dense_vector<Scalar> x(rows, 1.0);
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mtl::dense_vector<Scalar> b(rows, 1.0);
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BENCH(x = mtl::upper_trisolve(m1,b);)
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std::cout << " colmajor^-1 * b:\t" << timer.value() << endl;
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// std::cerr << x << "\n";
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BENCH(x = mtl::upper_trisolve(m2,b);)
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std::cout << " rowmajor^-1 * b:\t" << timer.value() << endl;
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// std::cerr << x << "\n";
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}
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#endif
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}
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#endif
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#if 0
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// bench small matrices (in-place versus return bye value)
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{
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timer.reset();
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for (int _j=0; _j<10; ++_j) {
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Matrix4f m = Matrix4f::random();
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Vector4f b = Vector4f::random();
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Vector4f x = Vector4f::random();
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timer.start();
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for (int _k=0; _k<1000000; ++_k) {
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b = m.inverseProduct(b);
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}
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timer.stop();
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}
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std::cout << "4x4 :\t" << timer.value() << endl;
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}
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{
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timer.reset();
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for (int _j=0; _j<10; ++_j) {
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Matrix4f m = Matrix4f::random();
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Vector4f b = Vector4f::random();
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Vector4f x = Vector4f::random();
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timer.start();
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for (int _k=0; _k<1000000; ++_k) {
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m.inverseProductInPlace(x);
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}
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timer.stop();
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}
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std::cout << "4x4 IP :\t" << timer.value() << endl;
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}
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#endif
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std::cout << "\n\n";
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return 0;
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}
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