Towards Automatic Load Balancing for Programming Parallel Fuzzy Expert Systems in Heterogeneous Clusters

Abstract

FuzzyCLIPS is a rule-based language for developing the fuzzy expert systems. Due to the characteristics of rule-based languages, the execution of the system would be more time-consuming than most conventional algorithmic languages. To address this problem, we propose to execute a FuzzyCLIPS application in parallel on the emerging heterogeneous cluster system. Furthermore, to maximize the speedup of the parallel execution and to minimize the burden of programmers, we have implemented built-in self-scheduling schemes in the interpreter for better load balancing. The developer only needs to use our proposed directives to specify where the parallelisms are, no explicit and complicated send and receive routines have to be invoked in the parallel program. Experimental results show that the built-in load balancing schemes can improve the system performance significantly.