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https://gitlab.com/libeigen/eigen.git
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298 lines
8.3 KiB
C++
298 lines
8.3 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 1
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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_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_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 = false;
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//float density = float(NBPERROW)/float(SIZE);
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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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for (int i=0; i<cols*NBPERROW; ++i)
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{
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coords.push_back(Vector2i(ei_random<int>(0,rows-1),ei_random<int>(0,cols-1)));
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values.push_back(ei_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(ei_random<int>(0,rows-1),j));
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values.push_back(ei_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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timer.reset();
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timer.start();
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for (int k=0; k<REPEAT; ++k)
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setrand_eigen_dense(coords,values);
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timer.stop();
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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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timer.reset();
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timer.start();
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for (int k=0; k<REPEAT; ++k)
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setinnerrand_eigen(coords,values);
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timer.stop();
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std::cout << "Eigen fillrand\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_eigen_gnu_hash(coords,values);
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timer.stop();
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std::cout << "Eigen std::map\t" << timer.value() << "\n";
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}
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#ifndef NOGOOGLE
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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_eigen_google_dense(coords,values);
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timer.stop();
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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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timer.reset();
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timer.start();
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for (int k=0; k<REPEAT; ++k)
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setrand_eigen_google_sparse(coords,values);
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timer.stop();
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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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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_mapped(coords,values);
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timer.stop();
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std::cout << "ublas mapped\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_genvec(coords,values);
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timer.stop();
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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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timer.reset();
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timer.start();
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for (int k=0; k<REPEAT; ++k)
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setrand_mtl(coords,values);
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timer.stop();
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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.fillrand(coords[i].x(), coords[i].y()) = vals[i];
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}
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mat.endFill();
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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_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 0;//&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 0;//&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 0;//&mat.coeffRef(coords[0].x(), coords[0].y());
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}
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#endif
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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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