Parallelizing an experiment to decide shellability on bipartite graphs using Apache Spark
DOI:
https://doi.org/10.22579/20112629.428Keywords:
Apache™ Hadoop®, Apache Spark™, bipartite graph shellability, parallel experiments, unclassified NP problemsAbstract
Graph shellability is an NP problem whose classification either in P or in NP-complete remains unknown. In order to understand the computational behavior of graph shellability on bipartite graphs, as a particular case, it could be useful to develop an efficient way to generate and analyze results over sets of shellable and non-shellable instances. In this way, a sequentially designed exponential time experiment for deciding shellability on randomly generated instances was proposed in literature. In this work, with the aim of improving the performance of that experiment, we propose three alternative approaches using Apache Spark™, we called multi-core, multi-node and full-parallel. We tested and compared their execution time for bipartite graphs with 10,12,15,20 and 50 vertices with regard to the original version, and we got speedups between 1.37 and 1.67 for the first one, between 2.34 and 3.56 for the second one, and between 2.37 y 3.12 for the last version. The results suggest that parallelization could relieve the large execution times of the original approach.Downloads
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