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• W2039724126 abstract "MapReduce is a widely accepted framework for addressing big data challenges. Recently, it has also gained broad attention from scientists at the U.S. leadership computing facilities as a promising solution to process gigantic simulation results. However, conventional high-end computing systems are constructed based on the compute-centric paradigm while big data analytics applications prefer a data-centric paradigm such as MapReduce. This work characterizes the performance impact of key differences between compute- and data-centric paradigms and then provides optimizations to enable a dual-purpose HPC system that can efficiently support conventional HPC applications and new data analytics applications. Using a state-of-the-art MapReduce implementation Spark and the Hyperion system at Lawrence Livermore National Laboratory, we have examined the impact of storage architectures, data locality and task scheduling to the memory-resident MapReduce jobs. Based on our characterization and findings of the performance behaviors, we have introduced two optimization techniques, namely Enhanced Load Balancer and Congestion-Aware Task Dispatching, to improve the performance of Spark applications." @default.
• W2039724126 created "2016-06-24" @default.
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• W2039724126 date "2014-05-01" @default.
• W2039724126 modified "2023-09-23" @default.
• W2039724126 title "Characterization and Optimization of Memory-Resident MapReduce on HPC Systems" @default.
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• W2039724126 doi "https://doi.org/10.1109/ipdps.2014.87" @default.
• W2039724126 hasPublicationYear "2014" @default.
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