Improving Performance of Large Sparse Linear System Solvers On Distributed Memory Systems By Asynchronous Algorithms


The KIPS Transactions:PartA, Vol. 8, No. 4, pp. 439-446, Dec. 2001
10.3745/KIPSTA.2001.8.4.439,   PDF Download:

Abstract

The main stream of parallel programming today is using synchronous algorithms, where processor synchronization for correct computation and workload balance are essential. Overall performance of the whole system is dependent upon the performance of the slowest processor, if workload is not well-balanced or heterogeneous clusters are used. Asynchronous iteration is a way to mitigate such problems, but most of the works done so far are for shared memory systems. In this paper, we suggest and implement a parallel large sparse linear system solver that improves performance on distributed memory systems like clusters by reducing processor idle times as much as possible by asynchronous iterations.


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Cite this article
[IEEE Style]
P. S. Park and S. C. Shin, "Improving Performance of Large Sparse Linear System Solvers On Distributed Memory Systems By Asynchronous Algorithms," The KIPS Transactions:PartA, vol. 8, no. 4, pp. 439-446, 2001. DOI: 10.3745/KIPSTA.2001.8.4.439.

[ACM Style]
Pil Seong Park and Soon Churl Shin. 2001. Improving Performance of Large Sparse Linear System Solvers On Distributed Memory Systems By Asynchronous Algorithms. The KIPS Transactions:PartA, 8, 4, (2001), 439-446. DOI: 10.3745/KIPSTA.2001.8.4.439.