A New Domain Decomposition Approach Suited for Grid Computing

Acebrón, Juan A.; Durán, Raúl; Rico, Rafael García; Spigler, Renato

doi:10.1007/978-3-540-75755-9_91

Cited by 2 publications

(1 citation statement)

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“…Stated in such terms, clearly, the method appears to be scalable as well. Even though the probabilistic algorithm we derived and considered here is scalable, naturally fault tolerant [8], and well suited to grid and heterogeneous computing [5], it suffers for some weakness, due to the inherently poor accuracy of all Monte Carlo methods. A considerable improvement, however, can be achieved using sequences of "quasirandom numbers" [6,12] instead of sequences of pseudorandom numbers.…”

Section: The Probabilistic Methodsmentioning

confidence: 99%

Scalability and Performance Analysis of a Probabilistic Domain Decomposition Method

Acebrón

Spigler

Parallel Processing and Applied Mathematics

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In this paper, we analyze the scalability and performance of a probabilistic domain decomposition strategy for solving linear elliptic boundary-value problems. Such a strategy consists of a hybrid numerical scheme based on a probabilistic method along with a domain decomposition, and full decoupling can be accomplished. It is shown that such a method performs well for an arbitrarily large number of processors, while the classical deterministic approach is strongly affected by intercommunications. Therefore, the overall performance degrades dramatically for rather large number of processors. Furthermore, we find that the probabilistic method is scalable as the number of subdomains, i.e., the number of processors involved, increases. This fact is clearly illustrated by an example.

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