324 lines
8.8 KiB
C++
324 lines
8.8 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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#include <typeinfo>
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#ifndef SIZE
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#define SIZE 1000000
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#endif
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#ifndef NNZPERCOL
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#define NNZPERCOL 6
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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 <algorithm>
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#include "BenchTimer.h"
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#include "BenchUtil.h"
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#include "BenchSparseUtil.h"
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#ifndef NBTRIES
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#define NBTRIES 1
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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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// #ifdef MKL
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//
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// #include "mkl_types.h"
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// #include "mkl_spblas.h"
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//
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// template<typename Lhs,typename Rhs,typename Res>
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// void mkl_multiply(const Lhs& lhs, const Rhs& rhs, Res& res)
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// {
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// char n = 'N';
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// float alpha = 1;
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// char matdescra[6];
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// matdescra[0] = 'G';
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// matdescra[1] = 0;
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// matdescra[2] = 0;
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// matdescra[3] = 'C';
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// mkl_scscmm(&n, lhs.rows(), rhs.cols(), lhs.cols(), &alpha, matdescra,
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// lhs._valuePtr(), lhs._innerIndexPtr(), lhs.outerIndexPtr(),
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// pntre, b, &ldb, &beta, c, &ldc);
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// // mkl_somatcopy('C', 'T', lhs.rows(), lhs.cols(), 1,
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// // lhs._valuePtr(), lhs.rows(), DST, dst_stride);
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// }
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//
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// #endif
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#ifdef CSPARSE
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cs* cs_sorted_multiply(const cs* a, const cs* b)
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{
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// return cs_multiply(a,b);
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cs* A = cs_transpose(a, 1);
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cs* B = cs_transpose(b, 1);
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cs* D = cs_multiply(B,A); /* D = B'*A' */
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cs_spfree (A) ;
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cs_spfree (B) ;
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cs_dropzeros (D) ; /* drop zeros from D */
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cs* C = cs_transpose (D, 1) ; /* C = D', so that C is sorted */
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cs_spfree (D) ;
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return C;
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// cs* A = cs_transpose(a, 1);
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// cs* C = cs_transpose(A, 1);
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// return C;
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}
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cs* cs_sorted_multiply2(const cs* a, const cs* b)
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{
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cs* D = cs_multiply(a,b);
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cs* E = cs_transpose(D,1);
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cs_spfree(D);
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cs* C = cs_transpose(E,1);
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cs_spfree(E);
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return C;
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}
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#endif
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void bench_sort();
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int main(int argc, char *argv[])
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{
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// bench_sort();
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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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EigenSparseMatrix sm1(rows,cols), sm2(rows,cols), sm3(rows,cols), sm4(rows,cols);
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BenchTimer timer;
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for (int nnzPerCol = NNZPERCOL; nnzPerCol>1; nnzPerCol/=1.1)
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{
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sm1.setZero();
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sm2.setZero();
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fillMatrix2(nnzPerCol, rows, cols, sm1);
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fillMatrix2(nnzPerCol, rows, cols, sm2);
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// std::cerr << "filling OK\n";
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// dense matrices
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#ifdef DENSEMATRIX
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{
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std::cout << "Eigen Dense\t" << nnzPerCol << "%\n";
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DenseMatrix m1(rows,cols), m2(rows,cols), m3(rows,cols);
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eiToDense(sm1, m1);
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eiToDense(sm2, m2);
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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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m3 = m1 * m2;
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timer.stop();
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std::cout << " a * b:\t" << timer.value() << endl;
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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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m3 = m1.transpose() * m2;
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timer.stop();
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std::cout << " a' * b:\t" << timer.value() << endl;
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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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m3 = m1.transpose() * m2.transpose();
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timer.stop();
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std::cout << " a' * b':\t" << timer.value() << endl;
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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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m3 = m1 * m2.transpose();
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timer.stop();
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std::cout << " a * b':\t" << timer.value() << endl;
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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" << sm1.nonZeros()/(float(sm1.rows())*float(sm1.cols()))*100 << "% * "
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<< sm2.nonZeros()/(float(sm2.rows())*float(sm2.cols()))*100 << "%\n";
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BENCH(sm3 = sm1 * sm2; )
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std::cout << " a * b:\t" << timer.value() << endl;
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// BENCH(sm3 = sm1.transpose() * sm2; )
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// std::cout << " a' * b:\t" << timer.value() << endl;
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// //
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// BENCH(sm3 = sm1.transpose() * sm2.transpose(); )
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// std::cout << " a' * b':\t" << timer.value() << endl;
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// //
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// BENCH(sm3 = sm1 * sm2.transpose(); )
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// std::cout << " a * b' :\t" << timer.value() << endl;
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// std::cout << "\n";
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//
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// BENCH( sm3._experimentalNewProduct(sm1, sm2); )
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// std::cout << " a * b:\t" << timer.value() << endl;
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//
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// BENCH(sm3._experimentalNewProduct(sm1.transpose(),sm2); )
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// std::cout << " a' * b:\t" << timer.value() << endl;
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// //
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// BENCH(sm3._experimentalNewProduct(sm1.transpose(),sm2.transpose()); )
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// std::cout << " a' * b':\t" << timer.value() << endl;
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// //
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// BENCH(sm3._experimentalNewProduct(sm1, sm2.transpose());)
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// std::cout << " a * b' :\t" << timer.value() << endl;
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}
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// eigen dyn-sparse matrices
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/*{
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DynamicSparseMatrix<Scalar> m1(sm1), m2(sm2), m3(sm3);
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std::cout << "Eigen dyn-sparse\t" << m1.nonZeros()/(float(m1.rows())*float(m1.cols()))*100 << "% * "
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<< m2.nonZeros()/(float(m2.rows())*float(m2.cols()))*100 << "%\n";
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// timer.reset();
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// timer.start();
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BENCH(for (int k=0; k<REPEAT; ++k) m3 = m1 * m2;)
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// timer.stop();
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std::cout << " a * b:\t" << timer.value() << endl;
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// std::cout << sm3 << "\n";
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timer.reset();
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timer.start();
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// std::cerr << "transpose...\n";
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// EigenSparseMatrix sm4 = sm1.transpose();
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// std::cout << sm4.nonZeros() << " == " << sm1.nonZeros() << "\n";
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// exit(1);
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// std::cerr << "transpose OK\n";
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// std::cout << sm1 << "\n\n" << sm1.transpose() << "\n\n" << sm4.transpose() << "\n\n";
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BENCH(for (int k=0; k<REPEAT; ++k) m3 = m1.transpose() * m2;)
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// timer.stop();
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std::cout << " a' * b:\t" << timer.value() << endl;
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// timer.reset();
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// timer.start();
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BENCH( for (int k=0; k<REPEAT; ++k) m3 = m1.transpose() * m2.transpose(); )
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// timer.stop();
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std::cout << " a' * b':\t" << timer.value() << endl;
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// timer.reset();
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// timer.start();
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BENCH( for (int k=0; k<REPEAT; ++k) m3 = m1 * m2.transpose(); )
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// timer.stop();
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std::cout << " a * b' :\t" << timer.value() << endl;
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}*/
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// CSparse
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#ifdef CSPARSE
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{
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std::cout << "CSparse \t" << nnzPerCol << "%\n";
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cs *m1, *m2, *m3;
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eiToCSparse(sm1, m1);
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eiToCSparse(sm2, m2);
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BENCH(
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{
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m3 = cs_sorted_multiply(m1, m2);
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if (!m3)
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{
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std::cerr << "cs_multiply failed\n";
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}
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// cs_print(m3, 0);
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cs_spfree(m3);
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}
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);
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// timer.stop();
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std::cout << " a * b:\t" << timer.value() << endl;
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// BENCH( { m3 = cs_sorted_multiply2(m1, m2); cs_spfree(m3); } );
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// std::cout << " a * b:\t" << timer.value() << endl;
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}
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#endif
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#ifndef NOUBLAS
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{
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std::cout << "ublas\t" << nnzPerCol << "%\n";
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UBlasSparse m1(rows,cols), m2(rows,cols), m3(rows,cols);
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eiToUblas(sm1, m1);
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eiToUblas(sm2, m2);
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BENCH(boost::numeric::ublas::prod(m1, m2, m3););
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std::cout << " a * b:\t" << timer.value() << endl;
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}
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#endif
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// GMM++
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#ifndef NOGMM
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{
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std::cout << "GMM++ sparse\t" << nnzPerCol << "%\n";
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GmmDynSparse gmmT3(rows,cols);
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GmmSparse m1(rows,cols), m2(rows,cols), m3(rows,cols);
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eiToGmm(sm1, m1);
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eiToGmm(sm2, m2);
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BENCH(gmm::mult(m1, m2, gmmT3););
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std::cout << " a * b:\t" << timer.value() << endl;
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// BENCH(gmm::mult(gmm::transposed(m1), m2, gmmT3););
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// std::cout << " a' * b:\t" << timer.value() << endl;
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//
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// if (rows<500)
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// {
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// BENCH(gmm::mult(gmm::transposed(m1), gmm::transposed(m2), gmmT3););
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// std::cout << " a' * b':\t" << timer.value() << endl;
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//
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// BENCH(gmm::mult(m1, gmm::transposed(m2), gmmT3););
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// std::cout << " a * b':\t" << timer.value() << endl;
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// }
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// else
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// {
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// std::cout << " a' * b':\t" << "forever" << endl;
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// std::cout << " a * b':\t" << "forever" << endl;
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// }
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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" << nnzPerCol << "%\n";
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MtlSparse m1(rows,cols), m2(rows,cols), m3(rows,cols);
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eiToMtl(sm1, m1);
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eiToMtl(sm2, m2);
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BENCH(m3 = m1 * m2;);
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std::cout << " a * b:\t" << timer.value() << endl;
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// BENCH(m3 = trans(m1) * m2;);
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// std::cout << " a' * b:\t" << timer.value() << endl;
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//
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// BENCH(m3 = trans(m1) * trans(m2););
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// std::cout << " a' * b':\t" << timer.value() << endl;
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//
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// BENCH(m3 = m1 * trans(m2););
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// std::cout << " a * b' :\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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}
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return 0;
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}
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