Titlebook: High Performance Computingfor Computational Science -- VECPAR 2010; 9th International Co José M. Laginha M. Palma,Michel Daydé,João Correia

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Multifrontal Computations on GPUs and Their Multi-core Hostswidely used applications. This paper examines how a multifrontal sparse solver performs when exploiting both the GPU and its multi-core host. It demonstrates that the GPU can dramatically accelerate the solver relative to one host CPU. Furthermore, the solver can profitably exploit both the GPU to f

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Accelerating GPU Kernels for Dense Linear Algebrare have to be highly optimized. We present some techniques and implementations that significantly accelerate the corresponding routines from currently available libraries for GPUs. In particular, . – a set of GPU specific optimization techniques – allows us to easily remove performance oscillations

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An Hybrid Approach for the Parallelization of a Block Iterative Algorithmection per subsystem and uses these projections to construct an approximation to the solution of the linear system..The usual parallelization strategy in block algorithms is to distribute the different blocks on the available processors. In this paper, we follow another approach where we do not perf

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Towards an Efficient Tile Matrix Inversion of Symmetric Positive Definite Matrices on Multicore Archy of algorithms, the ., has recently been introduced. Previous research has shown that it is possible to write efficient and scalable tile algorithms for performing a Cholesky factorization, a (pseudo) LU factorization, a QR factorization, and computing the inverse of a symmetric positive definite m

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