mirror of
https://gitlab.com/libeigen/eigen.git
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Clang-format tests, examples, libraries, benchmarks, etc.
This commit is contained in:
Antonio Sánchez
committed by
Rasmus Munk Larsen
Rasmus Munk Larsen
parent
3252ecc7a4
commit
46e9cdb7fe
@@ -1,8 +1,9 @@
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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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// g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.005 -DSIZE=10000 &&
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// ./a.out g++ -O3 -g0 -DNDEBUG sparse_product.cpp -I.. -I/home/gael/Coding/LinearAlgebra/mtl4/ -DDENSITY=0.05
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// -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 650000
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#endif
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@@ -25,163 +26,160 @@
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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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#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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} \
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timer.stop(); \
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}
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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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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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cs* cs_sorted_multiply(const cs* a, const cs* 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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}
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#endif
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int main(int argc, char *argv[])
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{
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int main(int argc, char* argv[]) {
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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);
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EigenSparseMatrix sm1(rows, cols);
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DenseVector v1(cols), v2(cols);
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v1.setRandom();
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BenchTimer timer;
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for (float density = DENSITY; density>=MINDENSITY; density*=0.5)
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{
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//fillMatrix(density, rows, cols, sm1);
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for (float density = DENSITY; density >= MINDENSITY; density *= 0.5) {
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// fillMatrix(density, rows, cols, sm1);
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fillMatrix2(7, rows, cols, sm1);
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// dense matrices
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#ifdef DENSEMATRIX
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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" << density*100 << "%\n";
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DenseMatrix m1(rows,cols);
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std::cout << "Eigen Dense\t" << density * 100 << "%\n";
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DenseMatrix m1(rows, cols);
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eiToDense(sm1, m1);
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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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v2 = m1 * v1;
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for (int k = 0; k < REPEAT; ++k) v2 = m1 * v1;
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timer.stop();
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std::cout << " a * v:\t" << timer.best() << " " << double(REPEAT)/timer.best() << " * / sec " << endl;
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std::cout << " a * v:\t" << timer.best() << " " << double(REPEAT) / timer.best() << " * / sec " << 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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v2 = m1.transpose() * v1;
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for (int k = 0; k < REPEAT; ++k) v2 = m1.transpose() * v1;
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timer.stop();
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std::cout << " a' * v:\t" << timer.best() << endl;
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}
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#endif
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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()*sm1.cols())*100 << "%\n";
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std::cout << "Eigen sparse\t" << sm1.nonZeros() / float(sm1.rows() * sm1.cols()) * 100 << "%\n";
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BENCH(asm("#myc"); v2 = sm1 * v1; asm("#myd");)
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std::cout << " a * v:\t" << timer.best()/REPEAT << " " << double(REPEAT)/timer.best(REAL_TIMER) << " * / sec " << endl;
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std::cout << " a * v:\t" << timer.best() / REPEAT << " " << double(REPEAT) / timer.best(REAL_TIMER)
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<< " * / sec " << endl;
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BENCH({
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asm("#mya");
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v2 = sm1.transpose() * v1;
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asm("#myb");
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})
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BENCH( { asm("#mya"); v2 = sm1.transpose() * v1; asm("#myb"); })
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std::cout << " a' * v:\t" << timer.best()/REPEAT << endl;
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std::cout << " a' * v:\t" << timer.best() / REPEAT << endl;
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}
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// {
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// DynamicSparseMatrix<Scalar> m1(sm1);
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// std::cout << "Eigen dyn-sparse\t" << m1.nonZeros()/float(m1.rows()*m1.cols())*100 << "%\n";
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//
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// BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1 * v1;)
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// std::cout << " a * v:\t" << timer.value() << endl;
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//
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// BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1.transpose() * v1;)
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// std::cout << " a' * v:\t" << timer.value() << endl;
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// }
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// {
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// DynamicSparseMatrix<Scalar> m1(sm1);
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// std::cout << "Eigen dyn-sparse\t" << m1.nonZeros()/float(m1.rows()*m1.cols())*100 << "%\n";
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//
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// BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1 * v1;)
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// std::cout << " a * v:\t" << timer.value() << endl;
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//
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// BENCH(for (int k=0; k<REPEAT; ++k) v2 = m1.transpose() * v1;)
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// std::cout << " a' * v:\t" << timer.value() << endl;
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// }
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// GMM++
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#ifndef NOGMM
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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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//GmmDynSparse gmmT3(rows,cols);
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GmmSparse m1(rows,cols);
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std::cout << "GMM++ sparse\t" << density * 100 << "%\n";
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// GmmDynSparse gmmT3(rows,cols);
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GmmSparse m1(rows, cols);
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eiToGmm(sm1, m1);
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std::vector<Scalar> gmmV1(cols), gmmV2(cols);
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Map<Matrix<Scalar,Dynamic,1> >(&gmmV1[0], cols) = v1;
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Map<Matrix<Scalar,Dynamic,1> >(&gmmV2[0], cols) = v2;
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Map<Matrix<Scalar, Dynamic, 1> >(&gmmV1[0], cols) = v1;
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Map<Matrix<Scalar, Dynamic, 1> >(&gmmV2[0], cols) = v2;
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BENCH( asm("#myx"); gmm::mult(m1, gmmV1, gmmV2); asm("#myy"); )
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BENCH(asm("#myx"); gmm::mult(m1, gmmV1, gmmV2); asm("#myy");)
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std::cout << " a * v:\t" << timer.value() << endl;
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BENCH( gmm::mult(gmm::transposed(m1), gmmV1, gmmV2); )
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BENCH(gmm::mult(gmm::transposed(m1), gmmV1, gmmV2);)
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std::cout << " a' * v:\t" << timer.value() << endl;
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}
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#endif
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#ifndef NOUBLAS
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#endif
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#ifndef NOUBLAS
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{
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std::cout << "ublas sparse\t" << density*100 << "%\n";
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UBlasSparse m1(rows,cols);
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std::cout << "ublas sparse\t" << density * 100 << "%\n";
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UBlasSparse m1(rows, cols);
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eiToUblas(sm1, m1);
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boost::numeric::ublas::vector<Scalar> uv1, uv2;
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eiToUblasVec(v1,uv1);
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eiToUblasVec(v2,uv2);
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eiToUblasVec(v1, uv1);
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eiToUblasVec(v2, uv2);
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// std::vector<Scalar> gmmV1(cols), gmmV2(cols);
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// Map<Matrix<Scalar,Dynamic,1> >(&gmmV1[0], cols) = v1;
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// Map<Matrix<Scalar,Dynamic,1> >(&gmmV2[0], cols) = v2;
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// std::vector<Scalar> gmmV1(cols), gmmV2(cols);
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// Map<Matrix<Scalar,Dynamic,1> >(&gmmV1[0], cols) = v1;
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// Map<Matrix<Scalar,Dynamic,1> >(&gmmV2[0], cols) = v2;
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BENCH( uv2 = boost::numeric::ublas::prod(m1, uv1); )
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BENCH(uv2 = boost::numeric::ublas::prod(m1, uv1);)
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std::cout << " a * v:\t" << timer.value() << endl;
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// BENCH( boost::ublas::prod(gmm::transposed(m1), gmmV1, gmmV2); )
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// std::cout << " a' * v:\t" << timer.value() << endl;
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// BENCH( boost::ublas::prod(gmm::transposed(m1), gmmV1, gmmV2); )
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// std::cout << " a' * v:\t" << timer.value() << endl;
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}
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#endif
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#endif
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// MTL4
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#ifndef NOMTL
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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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std::cout << "MTL4\t" << density * 100 << "%\n";
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MtlSparse m1(rows, cols);
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eiToMtl(sm1, m1);
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mtl::dense_vector<Scalar> mtlV1(cols, 1.0);
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mtl::dense_vector<Scalar> mtlV2(cols, 1.0);
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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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mtlV2 = m1 * mtlV1;
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for (int k = 0; k < REPEAT; ++k) mtlV2 = m1 * mtlV1;
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timer.stop();
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std::cout << " a * v:\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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mtlV2 = trans(m1) * mtlV1;
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for (int k = 0; k < REPEAT; ++k) mtlV2 = trans(m1) * mtlV1;
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timer.stop();
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std::cout << " a' * v:\t" << timer.value() << endl;
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}
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#endif
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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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