• Ocean and Polar Research
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• v.23 no.2
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• pp.173-179
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• 2001

Parallel supercomputing is now a must for oceanographic numerical modelers. Most of today's parallel numerical schemes use synchronous algorithms, where some processors that have finished their tasks earlier than others must wait at synchronization points for correct computation. Hence, the load balancing is a crucial factor, however, it is, in general, difficult to achieve on heterogeneous workstation clusters. We devise an asynchronous algorithm that reduces the idle times of faster processors, and discuss application of the algorithm to some grid problems and implementation on a workstation cluster using Message Passing Interface (MPI).

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.439-446
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• 2001

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.