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Efficient data access strategies for Hadoop and Spark on HPC cluster with heterogeneous storage.

, , , and . IEEE BigData, page 223-232. IEEE Computer Society, (2016)

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In-memory I/O and replication for HDFS with Memcached: Early experiences., , , , and . IEEE BigData, page 213-218. IEEE Computer Society, (2014)Can Non-volatile Memory Benefit MapReduce Applications on HPC Clusters?, , , and . PDSW-DISCS@SC, page 19-24. IEEE Computer Society, (2016)Performance Modeling for RDMA-Enhanced Hadoop MapReduce., , , and . ICPP, page 50-59. IEEE Computer Society, (2014)A Micro-benchmark Suite for Evaluating HDFS Operations on Modern Clusters., , , , and . WBDB, volume 8163 of Lecture Notes in Computer Science, page 129-147. Springer, (2012)MR-Advisor: A Comprehensive Tuning Tool for Advising HPC Users to Accelerate MapReduce Applications on Supercomputers., , , , and . SBAC-PAD, page 198-205. IEEE Computer Society, (2016)HOMR: a hybrid approach to exploit maximum overlapping in MapReduce over high performance interconnects., , , and . ICS, page 33-42. ACM, (2014)NVMD: Non-volatile memory assisted design for accelerating MapReduce and DAG execution frameworks on HPC systems., , , and . IEEE BigData, page 369-374. IEEE Computer Society, (2017)A Plugin-Based Approach to Exploit RDMA Benefits for Apache and Enterprise HDFS., , , , , and . BPOE, volume 9495 of Lecture Notes in Computer Science, page 119-132. Springer, (2015)Performance characterization and acceleration of in-memory file systems for Hadoop and Spark applications on HPC clusters., , , , and . IEEE BigData, page 243-252. IEEE Computer Society, (2015)High-Performance Design of YARN MapReduce on Modern HPC Clusters with Lustre and RDMA., , , , and . IPDPS, page 291-300. IEEE Computer Society, (2015)