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GPU-based parallel algorithms for sparse nonlinear systems
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DOI：10.1016/j.jpdc.2011.10.016.png)
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Abstract
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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.
Keywords:
GPGPU
GPU libraries
Multicore architectures
Nonlinear conjugate gradient algorithms
Parallel preconditioners
Bratu problem
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