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High performance finite element approximate inverse preconditioning
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DOI：10.1016/j.amc.2007.12.023.png)
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Abstract
En 中文
A new parallel normalized optimized approximate inverse algorithm, based on the concept of the fish bone computational approach satisfying an antidiagonal data dependency, for computing classes of explicit approximate inverses, is introduced for symmetric multiprocessor systems. The parallel normalized explicit approximate inverses are used in conjunction with parallel normalized explicit preconditioned conjugate gradient square schemes, for the efficient solution of finite element sparse linear systems. The parallel design and implementation issues of the new proposed algorithms are discussed and the parallel performance is presented, using OpenMP. (c) 2007 Elsevier Inc. All rights reserved.
Keywords:
algorithm design and analysis
concurrent programming
numerical algorithms and problems
sparse linear systems
iterative solution techniques
parallel algorithms
parallelism and concurrency
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3.4
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