sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Fault Tolerant MapReduce-MPI for HPC Clusters

SESSION: Cloud Resource Management


EVENT TAG(S): Clouds and Distributed Computing

TIME: 11:30AM - 12:00PM


AUTHOR(S):Yanfei Guo, Wesley Bland, Pavan Balaji, Xiaobo Zhou



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%.

Chair/Author Details:

Brent Welch (Chair) - Google|

Yanfei Guo - University of Colorado Colorado Springs

Wesley Bland - Argonne National Laboratory

Pavan Balaji - Argonne National Laboratory

Xiaobo Zhou - University of Colorado Colorado Springs

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society