486 lines
13 KiB
C++
486 lines
13 KiB
C++
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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 -DCSPARSE
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// -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
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#ifndef SIZE
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#define SIZE 100000
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#endif
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#ifndef NBPERROW
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#define NBPERROW 24
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#endif
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#ifndef REPEAT
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#define REPEAT 2
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#endif
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#ifndef NBTRIES
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#define NBTRIES 2
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#endif
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#ifndef KK
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#define KK 10
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#endif
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#ifndef NOGOOGLE
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#define EIGEN_GOOGLEHASH_SUPPORT
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#include <google/sparse_hash_map>
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#endif
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#include "BenchSparseUtil.h"
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#define CHECK_MEM
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// #define CHECK_MEM std/**/::cout << "check mem\n"; getchar();
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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 std::vector<Vector2i> Coordinates;
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typedef std::vector<float> Values;
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EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_eigen_dynamic(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_eigen_compact(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_eigen_sumeq(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_scipy(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_ublas_mapped(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_ublas_coord(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_ublas_compressed(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_ublas_genvec(const Coordinates& coords, const Values& vals);
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EIGEN_DONT_INLINE Scalar* setrand_mtl(const Coordinates& coords, const Values& vals);
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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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bool fullyrand = true;
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BenchTimer timer;
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Coordinates coords;
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Values values;
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if(fullyrand)
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{
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Coordinates pool;
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pool.reserve(cols*NBPERROW);
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std::cerr << "fill pool" << "\n";
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for (int i=0; i<cols*NBPERROW; )
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{
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// DynamicSparseMatrix<int> stencil(SIZE,SIZE);
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Vector2i ij(internal::random<int>(0,rows-1),internal::random<int>(0,cols-1));
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// if(stencil.coeffRef(ij.x(), ij.y())==0)
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{
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// stencil.coeffRef(ij.x(), ij.y()) = 1;
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pool.push_back(ij);
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}
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++i;
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}
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std::cerr << "pool ok" << "\n";
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int n = cols*NBPERROW*KK;
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coords.reserve(n);
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values.reserve(n);
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for (int i=0; i<n; ++i)
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{
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int i = internal::random<int>(0,pool.size());
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coords.push_back(pool[i]);
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values.push_back(internal::random<Scalar>());
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}
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}
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else
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{
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for (int j=0; j<cols; ++j)
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for (int i=0; i<NBPERROW; ++i)
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{
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coords.push_back(Vector2i(internal::random<int>(0,rows-1),j));
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values.push_back(internal::random<Scalar>());
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}
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}
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std::cout << "nnz = " << coords.size() << "\n";
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CHECK_MEM
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// dense matrices
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#ifdef DENSEMATRIX
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{
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BENCH(setrand_eigen_dense(coords,values);)
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std::cout << "Eigen Dense\t" << timer.value() << "\n";
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}
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#endif
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// eigen sparse matrices
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// if (!fullyrand)
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// {
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// BENCH(setinnerrand_eigen(coords,values);)
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// std::cout << "Eigen fillrand\t" << timer.value() << "\n";
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// }
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{
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BENCH(setrand_eigen_dynamic(coords,values);)
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std::cout << "Eigen dynamic\t" << timer.value() << "\n";
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}
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// {
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// BENCH(setrand_eigen_compact(coords,values);)
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// std::cout << "Eigen compact\t" << timer.value() << "\n";
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// }
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{
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BENCH(setrand_eigen_sumeq(coords,values);)
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std::cout << "Eigen sumeq\t" << timer.value() << "\n";
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}
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{
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// BENCH(setrand_eigen_gnu_hash(coords,values);)
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// std::cout << "Eigen std::map\t" << timer.value() << "\n";
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}
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{
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BENCH(setrand_scipy(coords,values);)
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std::cout << "scipy\t" << timer.value() << "\n";
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}
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#ifndef NOGOOGLE
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{
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BENCH(setrand_eigen_google_dense(coords,values);)
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std::cout << "Eigen google dense\t" << timer.value() << "\n";
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}
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{
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BENCH(setrand_eigen_google_sparse(coords,values);)
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std::cout << "Eigen google sparse\t" << timer.value() << "\n";
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}
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#endif
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#ifndef NOUBLAS
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{
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// BENCH(setrand_ublas_mapped(coords,values);)
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// std::cout << "ublas mapped\t" << timer.value() << "\n";
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}
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{
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BENCH(setrand_ublas_genvec(coords,values);)
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std::cout << "ublas vecofvec\t" << timer.value() << "\n";
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}
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/*{
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timer.reset();
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timer.start();
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for (int k=0; k<REPEAT; ++k)
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setrand_ublas_compressed(coords,values);
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timer.stop();
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std::cout << "ublas comp\t" << timer.value() << "\n";
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}
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{
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timer.reset();
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timer.start();
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for (int k=0; k<REPEAT; ++k)
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setrand_ublas_coord(coords,values);
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timer.stop();
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std::cout << "ublas coord\t" << timer.value() << "\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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BENCH(setrand_mtl(coords,values));
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std::cout << "MTL\t" << timer.value() << "\n";
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}
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#endif
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return 0;
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}
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EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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SparseMatrix<Scalar> mat(SIZE,SIZE);
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//mat.startFill(2000000/*coords.size()*/);
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for (int i=0; i<coords.size(); ++i)
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{
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mat.insert(coords[i].x(), coords[i].y()) = vals[i];
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}
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mat.finalize();
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CHECK_MEM;
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return 0;
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}
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EIGEN_DONT_INLINE Scalar* setrand_eigen_dynamic(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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DynamicSparseMatrix<Scalar> mat(SIZE,SIZE);
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mat.reserve(coords.size()/10);
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for (int i=0; i<coords.size(); ++i)
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{
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mat.coeffRef(coords[i].x(), coords[i].y()) += vals[i];
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}
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mat.finalize();
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CHECK_MEM;
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return &mat.coeffRef(coords[0].x(), coords[0].y());
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}
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EIGEN_DONT_INLINE Scalar* setrand_eigen_sumeq(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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int n = coords.size()/KK;
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DynamicSparseMatrix<Scalar> mat(SIZE,SIZE);
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for (int j=0; j<KK; ++j)
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{
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DynamicSparseMatrix<Scalar> aux(SIZE,SIZE);
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mat.reserve(n);
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for (int i=j*n; i<(j+1)*n; ++i)
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{
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aux.insert(coords[i].x(), coords[i].y()) += vals[i];
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}
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aux.finalize();
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mat += aux;
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}
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return &mat.coeffRef(coords[0].x(), coords[0].y());
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}
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EIGEN_DONT_INLINE Scalar* setrand_eigen_compact(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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DynamicSparseMatrix<Scalar> setter(SIZE,SIZE);
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setter.reserve(coords.size()/10);
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for (int i=0; i<coords.size(); ++i)
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{
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setter.coeffRef(coords[i].x(), coords[i].y()) += vals[i];
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}
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SparseMatrix<Scalar> mat = setter;
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CHECK_MEM;
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return &mat.coeffRef(coords[0].x(), coords[0].y());
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}
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EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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SparseMatrix<Scalar> mat(SIZE,SIZE);
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{
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RandomSetter<SparseMatrix<Scalar>, StdMapTraits > setter(mat);
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for (int i=0; i<coords.size(); ++i)
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{
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setter(coords[i].x(), coords[i].y()) += vals[i];
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}
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CHECK_MEM;
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}
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return &mat.coeffRef(coords[0].x(), coords[0].y());
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}
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#ifndef NOGOOGLE
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EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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SparseMatrix<Scalar> mat(SIZE,SIZE);
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{
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RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> setter(mat);
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for (int i=0; i<coords.size(); ++i)
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setter(coords[i].x(), coords[i].y()) += vals[i];
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CHECK_MEM;
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}
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return &mat.coeffRef(coords[0].x(), coords[0].y());
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}
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EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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SparseMatrix<Scalar> mat(SIZE,SIZE);
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{
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RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> setter(mat);
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for (int i=0; i<coords.size(); ++i)
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setter(coords[i].x(), coords[i].y()) += vals[i];
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CHECK_MEM;
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}
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return &mat.coeffRef(coords[0].x(), coords[0].y());
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}
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#endif
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template <class T>
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void coo_tocsr(const int n_row,
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const int n_col,
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const int nnz,
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const Coordinates Aij,
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const Values Ax,
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int Bp[],
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int Bj[],
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T Bx[])
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{
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//compute number of non-zero entries per row of A coo_tocsr
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std::fill(Bp, Bp + n_row, 0);
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for (int n = 0; n < nnz; n++){
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Bp[Aij[n].x()]++;
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}
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//cumsum the nnz per row to get Bp[]
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for(int i = 0, cumsum = 0; i < n_row; i++){
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int temp = Bp[i];
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Bp[i] = cumsum;
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cumsum += temp;
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}
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Bp[n_row] = nnz;
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//write Aj,Ax into Bj,Bx
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for(int n = 0; n < nnz; n++){
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int row = Aij[n].x();
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int dest = Bp[row];
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Bj[dest] = Aij[n].y();
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Bx[dest] = Ax[n];
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Bp[row]++;
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}
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for(int i = 0, last = 0; i <= n_row; i++){
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int temp = Bp[i];
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Bp[i] = last;
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last = temp;
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}
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//now Bp,Bj,Bx form a CSR representation (with possible duplicates)
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}
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template< class T1, class T2 >
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bool kv_pair_less(const std::pair<T1,T2>& x, const std::pair<T1,T2>& y){
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return x.first < y.first;
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}
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template<class I, class T>
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void csr_sort_indices(const I n_row,
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const I Ap[],
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I Aj[],
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T Ax[])
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{
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std::vector< std::pair<I,T> > temp;
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for(I i = 0; i < n_row; i++){
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I row_start = Ap[i];
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I row_end = Ap[i+1];
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temp.clear();
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for(I jj = row_start; jj < row_end; jj++){
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temp.push_back(std::make_pair(Aj[jj],Ax[jj]));
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}
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std::sort(temp.begin(),temp.end(),kv_pair_less<I,T>);
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for(I jj = row_start, n = 0; jj < row_end; jj++, n++){
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Aj[jj] = temp[n].first;
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Ax[jj] = temp[n].second;
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}
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}
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}
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template <class I, class T>
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void csr_sum_duplicates(const I n_row,
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const I n_col,
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I Ap[],
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I Aj[],
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T Ax[])
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{
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I nnz = 0;
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I row_end = 0;
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for(I i = 0; i < n_row; i++){
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I jj = row_end;
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row_end = Ap[i+1];
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while( jj < row_end ){
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I j = Aj[jj];
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T x = Ax[jj];
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jj++;
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while( jj < row_end && Aj[jj] == j ){
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x += Ax[jj];
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jj++;
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}
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Aj[nnz] = j;
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Ax[nnz] = x;
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nnz++;
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}
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Ap[i+1] = nnz;
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}
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}
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EIGEN_DONT_INLINE Scalar* setrand_scipy(const Coordinates& coords, const Values& vals)
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{
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using namespace Eigen;
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SparseMatrix<Scalar> mat(SIZE,SIZE);
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mat.resizeNonZeros(coords.size());
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// std::cerr << "setrand_scipy...\n";
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coo_tocsr<Scalar>(SIZE,SIZE, coords.size(), coords, vals, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr());
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// std::cerr << "coo_tocsr ok\n";
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csr_sort_indices(SIZE, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr());
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csr_sum_duplicates(SIZE, SIZE, mat._outerIndexPtr(), mat._innerIndexPtr(), mat._valuePtr());
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mat.resizeNonZeros(mat._outerIndexPtr()[SIZE]);
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return &mat.coeffRef(coords[0].x(), coords[0].y());
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}
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#ifndef NOUBLAS
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EIGEN_DONT_INLINE Scalar* setrand_ublas_mapped(const Coordinates& coords, const Values& vals)
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{
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using namespace boost;
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using namespace boost::numeric;
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using namespace boost::numeric::ublas;
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mapped_matrix<Scalar> aux(SIZE,SIZE);
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for (int i=0; i<coords.size(); ++i)
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{
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aux(coords[i].x(), coords[i].y()) += vals[i];
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}
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CHECK_MEM;
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compressed_matrix<Scalar> mat(aux);
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return 0;// &mat(coords[0].x(), coords[0].y());
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}
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/*EIGEN_DONT_INLINE Scalar* setrand_ublas_coord(const Coordinates& coords, const Values& vals)
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{
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using namespace boost;
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using namespace boost::numeric;
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using namespace boost::numeric::ublas;
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coordinate_matrix<Scalar> aux(SIZE,SIZE);
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for (int i=0; i<coords.size(); ++i)
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{
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aux(coords[i].x(), coords[i].y()) = vals[i];
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}
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compressed_matrix<Scalar> mat(aux);
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return 0;//&mat(coords[0].x(), coords[0].y());
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}
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EIGEN_DONT_INLINE Scalar* setrand_ublas_compressed(const Coordinates& coords, const Values& vals)
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{
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using namespace boost;
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using namespace boost::numeric;
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using namespace boost::numeric::ublas;
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compressed_matrix<Scalar> mat(SIZE,SIZE);
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for (int i=0; i<coords.size(); ++i)
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{
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mat(coords[i].x(), coords[i].y()) = vals[i];
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}
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return 0;//&mat(coords[0].x(), coords[0].y());
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}*/
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EIGEN_DONT_INLINE Scalar* setrand_ublas_genvec(const Coordinates& coords, const Values& vals)
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{
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using namespace boost;
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using namespace boost::numeric;
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using namespace boost::numeric::ublas;
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// ublas::vector<coordinate_vector<Scalar> > foo;
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generalized_vector_of_vector<Scalar, row_major, ublas::vector<coordinate_vector<Scalar> > > aux(SIZE,SIZE);
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for (int i=0; i<coords.size(); ++i)
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{
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aux(coords[i].x(), coords[i].y()) += vals[i];
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}
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CHECK_MEM;
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compressed_matrix<Scalar,row_major> mat(aux);
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return 0;//&mat(coords[0].x(), coords[0].y());
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}
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#endif
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#ifndef NOMTL
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EIGEN_DONT_INLINE void setrand_mtl(const Coordinates& coords, const Values& vals);
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#endif
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