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2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering

作者： Galiano, V. Migallon, H. Migallon, V. Penades, J. Univ Miguel Hernandez Dept Phys & Comp Architectures Alicante Spain Univ Alicante Dept Comp Sci & Artificial Intelligence Alacant Spain

ISBN: (纸本)9781905088423

In this paper we describe some parallel algorithms for solving nonlinear systems using CUDA (Compute Unified Device Architecture) over a GPU (Graphics Processing Unit). The proposed algorithms are based on both the Fletcher-Reeves version of the nonlinear conjugate gradient method and a polynomial preconditioner type based on block two-stage methods. Several strategies of parallelization and different storage formats for sparse matrices are discussed. The reported numerical experiments analyze the behavior of these algorithms working in a fine grain parallel environment compared with a thread-based environment.

关键词： GPGPU GPU libraries multicore nonlinear conjugate gradient algorithms parallel preconditioners ILU factorizations two-stage methods Bratu problem

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GPU-based parallel algorithms for sparse nonlinear systems

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2012年第9期72卷 1098-1105页

作者： Galiano, V. Migallon, H. Migallon, V. Penades, J. Univ Alicante Dept Comp Sci & Artificial Intelligence E-03071 Alicante Spain Univ Miguel Hernandez Dept Phys & Comp Architectures E-03202 Alicante Spain

In this work we describe some parallel algorithms for solving nonlinear systems using CUDA (Compute Unified Device Architecture) over a CPU (Graphics Processing Unit). The proposed algorithms are based on both the Fletcher-Reeves version of the nonlinear conjugate gradient method and a polynomial preconditioner type based on block two-stage methods. Several strategies of parallelization and different storage formats for sparse matrices are discussed. The reported numerical experiments analyze the behavior of these algorithms working in a fine grain parallel environment compared with a thread-based environment. (C) 2011 Elsevier Inc. All rights reserved.

关键词： GPGPU GPU libraries Multicore architectures nonlinear conjugate gradient algorithms Parallel preconditioners Bratu problem

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Parallel nonlinear preconditioners on multicore architectures

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JOURNAL OF SUPERCOMPUTING 2011年第2期58卷 160-167页

作者： Galiano, Vicente Migallon, Hector Migallon, Violeta Penades, Jose Univ Alicante Dept Ciencia Computac & Inteligencia Artificial Alicante 03071 Spain Univ Miguel Hernandez Dept Fis & Arquitectura Comp Alicante 03202 Spain

Parallel nonlinear preconditioners, for solving mildly nonlinear systems, are proposed. These algorithms are based on both the Fletcher-Reeves version of the nonlinear conjugate gradient method and a polynomial preconditioner type based on block two-stage methods. The behavior of these algorithms is analyzed when incomplete LU factorizations are used in order to obtain the inner splittings of the block two-stage method. As our illustrative example we have considered a nonlinear elliptic partial differential equation, known as the Bratu problem. The reported experiments show the performance of the algorithms designed in this work on two multicore architectures.

关键词： Multicore nonlinear conjugate gradient algorithms ILU factorizations Two-stage methods Bratu problem

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A Parallel High-Level Library for Solving Sparse nonlinear Systems

A Parallel High-Level Library for Solving Sparse Nonlinear S...

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7th International Conference on Engineering Computational Technology

作者： Migallon, H. Migallon, V. Penades, J. Univ Miguel Hernandez Dept Phys & Comp Architectures Alicante Spain Univ Alicante Dept Comp Sci & Artificial Intelligence Alicante Spain

ISBN: (纸本)9781905088416

In this paper we present a parallel library treated as a high-level interface for solving nonlinear systems. This work follows the guidelines of a high-level interface in terms of usability, but in fact it is a library developed using a mixed model in relation to the utilization of different programming languages. In order to create the high-level interfaces, we have chosen the Python language. On the other hand, the developed Fortran routines offer all the performance of the low-level language. The developed library, PyPANCG, consists of two modules, PySParNLCG and PySParNLPCG. The PySparNLCG module parallelizes the conjugate gradient method for solving the nonlinear system Ax = phi(x), and the PySParNLPCG module implements the preconditioning technique based on block two-stage methods. Experimental results report the numerical accuracy and the parallel performance of our approach on different parallel computers.

关键词： parallel libraries multicore nonlinear conjugate gradient algorithms parallel preconditioners Python high-level interfaces two-stage methods ILU factorizations

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