• 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).

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.1045-1058
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• 2000

In usual synchronous parallel computing, workload balance is a crucial factor to reduce idle times of some processors that have finished their jobs earlier than others. However, it is difficult to achieve on a heterogeneous workstation clusters where the available computing power of each processor is unpredictable. As a way to overcome such a problem, the idea of asynchronous methods has grown out and is being increasingly used and studied, but there is none for eigenvalue problems yet. In this paper, we suggest a new asynchronous method to solve some singular matrix problems, that can also be used for finding a certain eigenvector of some matrices.

• Journal of Internet Computing and Services
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• v.2 no.3
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• pp.81-92
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• 2001

The clustering environment with heterogeneous workstations provides the cost effectiveness and usability for executing applications in parallel. The load balancing is considered as a necessary feature for the clustering of heterogeneous workstations to minimize the turnaround time. Since each workstation may have different users, groups. requests for different tasks, and different processing power, the capability of each processing unit is relative to the others' unit in the clustering environment Previous works is a static approach which assign a predetermined weight for the processing capability of each workstation or a dynamic approach which executes a benchmark program to get relative processing capability of each workstation. The execution of the benchmark program, which has nothing to do with the application being executed, consumes the computation time and the overall turnaround time is delayed. In this paper, we present an efficient task distribution method and implementation of load balancing system for the clustering environment with heterogeneous workstations. Turnaround time of the methods presented in this paper is compared with the method without load balancing as well as with the method load balancing with performance evaluation program. The experimental results show that our methods outperform all the other methods that we compared.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2137-2153
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• 1998

네트워크로 연결된 워크스테이션들인 NOW(Network of Workstations)가 순차 프로그램과 병렬 프로그램들을 모두 효율적으로 수행할 수 있는 환경을 제공하려면 이 두 종류의 프로그램들이 어떻게 상호 영향을 주는지 이해할 필요가 있다. 본 논문에서는 순차 프로그램의 부하의 변화에 따른 병렬 프로그램의 성능 변화를 알아보았다. 이러한 목적으로 NOW 시뮬레이터를 구현하고 실험을 하였다. 구현에서 순차 프로그램과 병렬 프로그램들은 합성 부하를 사용하였고 병렬 프로그램의 수행 환경으로는 코스케쥴링과 프로세스 이주가 가능하다고 가정하였다. 실험을 통해서 순차 프로그램들에 의해 변하는 유휴 워트스테이션의 수가 병렬 프로그램의 성능에 어떠한 영향을 미치는지와 동시에 수행되는 여러 개의 병렬 프로그램들이 서로의 성능에 어떠한 영향을 미치는 가에 대해 결과를 수집하고 분석하였다.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.5
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• pp.496-505
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• 2000

Clusters of workstations (COW) are becoming an attractive option for parallel scientific computing, a field formerly reserved to the MPPs, because their cost-performance ratio is usuallybetter than that of comparable MPPS, and their hardware and software can be easily enhanced to thelatest generations. In this paper we present the design and implementation of our runtime library forclusters of workstations, called "Collective I/O Clustering". The library provides a friendlyprogramming model for the I/O of irregular applications on clusters of workstations, being completelyintegrated with the underlying communication and I/O system. In the collective I/O clustering, two I/Oconfigurations are possible. In the first I/O configuration, all processors allocated can act as I/Oservers as well as compute nodes. In the second I/O configuration, only a subset of processors canact as I/O servers, The compression and software caching facilities have been incorporated into thecollective 1/0 clustering to optimize the communication and I/O costs. All the performance results wereobtained on the IBM-SP machine, located at Argonne National Labs.