David M. Fernandez

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A study of the fundamental obstacles to accelerate the preconditioned conjugate gradient (PCG) method on modern graphic processing units (GPUs) is presented and several techniques are proposed to enhance its performance over previous work independent of the GPU generation and the matrix sparsity pattern. The proposed enhancements increase the performance of(More)
A wide class of finite-element (FE) electromagnetic applications requires computing very large sparse matrix vector multiplications (SMVM). Due to the sparsity pattern and size of the matrices, solvers can run relatively slowly. The rapid evolution of graphic processing units (GPUs) in performance, architecture, and programmability make them very attractive(More)
Accelerating numerical algorithms for solving sparse linear systems on parallel architectures has attracted the attention of many researchers due to their applicability to many engineering and scientific problems. The solution of sparse systems often dominates the overall execution time of such problems and is mainly solved by iterative methods.(More)
In this work we present a new alternate way to formulate the finite element method (FEM) for parallel processing based on the solution of single mesh elements called FEM-SES. The key idea is to decouple the solution of a single element from that of the whole mesh, thus exposing parallelism at the element level. Individual element solutions are then(More)
—Multicore systems are rapidly becoming a dominant industry trend for accelerating electromagnetics computations, driving researchers to address parallel programming paradigms early in application development. We present a new sparse representation and a two level partitioning scheme for efficient sparse matrix-vector multiplication on multicore systems,(More)
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