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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11712

Title: Extending FuzzyCLIPS for Parallelizing Data-Dependent Fuzzy Expert Systems
Authors: Wu, Chao-Chin;Lai, Lien-Fu;Chang, Yu-Shuo
Contributors: 資訊工程學系
Keywords: Rule-based expert system;FuzzyCLIPS;MPI;Cluster computing;Data dependence
Date: 2011-03
Issue Date: 2012-07-02T02:03:50Z
Publisher: Springer
Abstract: FuzzyCLIPS is a rule-based programming language and it is very suitable
for developing fuzzy expert systems. However, it usually requires much longer
execution time than algorithmic languages such as C and Java. To address this problem,
we propose a parallel version of FuzzyCLIPS to parallelize the execution of a
fuzzy expert system with data dependence on a cluster system. We have designed
some extended parallel syntax following the original FuzzyCLIPS style. To simplify
the programming model of parallel FuzzyCLIPS, we hide, as much as possible, the
tasks of parallel processing from programmers and implement them in the inference
engine by using MPI, the de facto standard for parallel programming for cluster systems.
Furthermore, a load balancing function has been implemented in the inference
engine to adapt to the heterogeneity of computing nodes. It will intelligently allocate
different amounts of workload to different computing nodes according to the results
of dynamic performance monitoring. The programmer only needs to invoke the function
in the program for better load balancing. To verify our design and evaluate the
performance, we have implemented a human resource website. Experimental results
show that the proposed parallel FuzzyCLIPS can garner a superlinear speedup and
provide a more reasonable response time.
Relation: Journal of Supercomputing, 59(3):1379-1395
Appears in Collections:[Department and Graduate Institute of Computer Science and Information Engineering] Periodical Articles

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