Parallel Evolutionary Approach of Compaction Problem Using MapReduce
A parallel evolutionary approach of Compaction Problem is introduced using MapReduce. This problem is of interest for VLSI testing and bioinformatics. The overall cost of a VLSI circuit’s testing depends on the length of its test sequence; therefore the reduction of this sequence, keeping the coverage, will lead to a reduction of used resources in the testing process. The problem of finding minimal test sets is NP-hard. We introduce a distributed evolutionary algorithm (MapReduce Parallel Evolutionary Algorithm−MRPEA) and compare it with two greedy approaches. The proposed algorithms are evaluated on randomly generated five-valued benchmarks that are scalable in size. The MapReduce paradigm offers the possibility to distribute and scale large amount of data. Experiments show the efficiency of the proposed parallel approach. The project, containing the Hadoop implementation can be found at: http://sourceforge.net/projects/dcpsolver/ .
KeywordsData Compaction Static Test Parallel Algorithm Evolution Strategies Greedy Discrete Optimization Apache Hadoop MapReduce Statistical Tests
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- 2.De Micheli, G.: Synthesis and Optimization of Digital Circuits. McGraw-Hill, Inc., New York (1994)Google Scholar
- 3.Drechsler, R.: Evolutionary Algorithms for VLSI CAD. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
- 5.Logofătu, D., Drechsler, R.: Comparative Study by Solving the Test Compaction Problem. In: Proceedings of the 38th International Symposium on Multiple Valued Logic, pp. 44–49 (2008)Google Scholar
- 6.Logofătu, D.: Static Test Compaction for VLSI Tests: An Evolutionary Approach. Advances in Electrical and Computer Engineering 8(2), 48–53 (2008)Google Scholar
- 7.Logofătu, D.: DNA Sequence Vectors and Their Compaction. In: AIP Conf. Proceedings of the 1st International Conference on Bio-Inspired Computational Methods Used for Solving Difficult Problems: Development of Intelligent and Complex Systems, vol. 1117(1), pp. 29–39 (2008)Google Scholar
- 9.Apache Hadoop (2009), http://hadoop.apache.org
- 10.Open Source Project, dopiSolver (2010), http://sourceforge.net/projects/dcpsolver/