Task Partitioning and Load Balancing Strategy for Matrix Applications on Distributed System - Volume 8 Number 3 (Mar. 2013) - JCOMPUTERS
Volume 8 Number 3 (Mar. 2013)
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JCP 2013 Vol.8(3): 576-584 ISSN: 1796-203X
doi: 10.4304/jcp.8.3.576-584

Task Partitioning and Load Balancing Strategy for Matrix Applications on Distributed System

Adeela Bashir1, Sajjad A. Madani1, Jawad Haider Kazmi1, and Kalim Qureshi2
1 Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad, Pakistan
2 Department of Information Science, Kuwait University, Kuwait


Abstract—In this paper, we present a load-balancing strategy (Adaptive Load Balancing strategy) for data parallel applications to balance the work load effectively on a distributed system. We study its impact on computation-hungry matrix multiplication application. The ALB strategy enhances the performance with features such as intelligent node selection, pre-task assignment, adaptive task sizing and buffer allocation, and load balancing. The ALB strategy exhibits reduced nodes idle time and inter process communication time, and improved speed up as compared to Run Time task Scheduling strategy.

Index Terms—task partitioning, load balancing, heterogeneous distributed systems, matrix multiplication, and performance evaluation

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Cite: Adeela Bashir, Sajjad A. Madani, Jawad Haider Kazmi, and Kalim Qureshi, " Task Partitioning and Load Balancing Strategy for Matrix Applications on Distributed System," Journal of Computers vol. 8, no. 3, pp. 576-584, 2013.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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