A distributed computing framework for all-to-all comparison problems

Zhang, Yi-Fan, Tian, Yu-Chu, Kelly, Wayne, & Fidge, Colin (2014) A distributed computing framework for all-to-all comparison problems. In IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Dallas, Texas, USA.


Distributed computation and storage have been widely used for processing of big data sets. For many big data problems, with the size of data growing rapidly, the distribution of computing tasks and related data can affect the performance of the computing system greatly. In this paper, a distributed computing framework is presented for high performance computing of All-to-All Comparison Problems. A data distribution strategy is embedded in the framework for reduced storage space and balanced computing load. Experiments are conducted to demonstrate the effectiveness of the developed approach. They have shown that about 88% of the ideal performance capacity have be achieved in multiple machines through using the approach presented in this paper.

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3 citations in Scopus
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ID Code: 78705
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: All-to-all comparison, distributed computing, computing framework, programming model, big data, data distribution
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300) > Computer Software not elsewhere classified (080399)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Distributed Computing not elsewhere classified (080599)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Deposited On: 17 Nov 2014 00:20
Last Modified: 17 Mar 2015 07:42

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