Fault tolerant MapReduce-MPI for HPC clusters

Abstract

Building MapReduce applications using the Message-Passing Interface (MPI) enables us to exploit the performance of large HPC clusters for big data analytics. However, due to the lacking of native fault tolerance support in MPI and the incompatibility between the MapReduce fault tolerance model and HPC schedulers, it is very hard to provide a fault tolerant MapReduce runtime for HPC clusters. We propose and develop FT-MRMPI, the first fault tolerant MapReduce framework on MPI for HPC clusters. We discover a unique way to perform failure detection and recovery by exploiting the current MPI semantics and the new proposal of user-level failure mitigation. We design and develop the checkpoint/restart model for fault tolerant MapReduce in MPI. We further tailor the detect/resume model to conserve work for more efficient fault tolerance. The experimental results on a 256-node HPC cluster show that FT-MRMPI effectively masks failures and reduces the job completion time by 39%.