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Showing 1 to 6 of 6 for “"Matrix Vector Multiply"”.

  1. Structure-based Optimizations for Sparse Matrix-Vector Multiply

    … and PBR, to improve the performance of Sparse Matrix-vector Multiply (SMVM) kernels, which dominate the runtime of iterative solvers for systems of linear equations. SMVM computations that use sparse formats typically achieve only a small fraction of peak CPU speeds because they are memory …

    vt Repository record for Structure-based Optimizations for Sparse Matrix-Vector Multiply (opens in a new tab)

  2. Electromagnetic simulations in frequency and time domain using adaptive integral method

    … scattering problems, a parallelized fast partial matrix solver for the simulation of very large scale microstrip circuits and the improved block aggregate matrix vector multiply for time domain adaptive integral equation method (TDAIM).

    nus Repository record for Electromagnetic simulations in frequency and time domain using adaptive integral method (opens in a new tab)

  3. Efficient Solution of Large Sparse Eigenvalue Problems in Microelectronic Simulation

    … of energy, plus the discretization and matrix-vector multiply routines. We adapt the algorithm to two important problems, the self-consistent Schrodinger-Poisson model of quantum-effect devices, and the vector Helmholtz equation for a dielectric waveguide, addressing other important …

    uiuc Repository record for Efficient Solution of Large Sparse Eigenvalue Problems in Microelectronic Simulation (opens in a new tab)

  4. Electromagnetic scattering and induction models for spheroidal geometries

    … and computational complexity, the Sparse Matrix/Canonical Grid (SMCG) method is applied to 3-D dense media scattering. By approximating the dyadic Green's function about a canonical rectilinear grid, weak interaction between spheroid far apart may be quickly approximated. Strong …

    mit Repository record for Electromagnetic scattering and induction models for spheroidal geometries (opens in a new tab)

  5. A parallel fill estimation algorithm for sparse matrices and tensors in blocked formats

    … times the time it took to compute one sparse matrix vector multiply, which was 61.176 times faster than OSKI. The maximum error generated by Phil was 0.0480, while OSKI sometimes produced estimates with a complete loss of accuracy. Finally, we find that Phil and OSKI produce comparable …

    mit Repository record for A parallel fill estimation algorithm for sparse matrices and tensors in blocked formats (opens in a new tab)

  6. Accelerating induction machine finite-element simulation with parallel processing

    … of the sparse iterative solution, and matrix-vector multiplication for magnetic flux density calculation. Due to the sparsity of the finite element problem, GPU-implementation of the sparse iterative solution did not result in faster computation times. The dominant speed-up achieved …

    uiuc Repository record for Accelerating induction machine finite-element simulation with parallel processing (opens in a new tab)