Abstract
Recently, MapReduce is getting deployed over many High Performance Computing (HPC) clusters. Different studies reveal that by leveraging the benefits of high-performance interconnects like InfiniBand in these clusters, faster MapReduce job execution can be obtained by using additional performance enhancing features. Although RDMA-enhanced MapReduce has been proven to provide faster solutions over Hadoop distributed file system, efficiencies over parallel file systems used in HPC clusters are yet to be discovered. In this paper, we present a complete methodology for evaluating MapReduce over Lustre file system to provide insights about the interactions of different system components in HPC clusters. Our performance evaluation shows that RDMA-enhanced MapReduce can achieve significant benefits in terms of execution time (49% in a 128-node HPC cluster) and resource utilization, compared to the default architecture. To the best of our knowledge, this is the first attempt to evaluate RDMA-enhanced MapReduce over Lustre file system on HPC clusters.
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