NDLI: High-Performance Design of YARN MapReduce on Modern HPC Clusters with Lustre and RDMA

Content Provider	IEEE Xplore Digital Library
Author	Wasi-ur-Rahman, M. Xiaoyi Lu Islam, N.S. Rajachandrasekar, R. Panda, D.K.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH, USA (Wasi-ur-Rahman, M.; Xiaoyi Lu; Islam, N.S.; Rajachandrasekar, R.; Panda, D.K.)
Abstract	The viability and benefits of running MapReduce over modern High Performance Computing (HPC) clusters, with high performance interconnects and parallel file systems, have attracted much attention in recent times due to its uniqueness of solving data analytics problems with a combination of Big Data and HPC technologies. Most HPC clusters follow the traditional Beowulf architecture with a separate parallel storage system (e.g. Lustre) and either no, or very limited, local storage. Since the MapReduce architecture relies heavily on the availability of local storage media, the Lustre-based global storage system in HPC clusters poses many new opportunities and challenges. In this paper, we propose a novel high-performance design for running YARN MapReduce on such HPC clusters by utilizing Lustre as the storage provider for intermediate data. We identify two different shuffle strategies, RDMA and Lustre Read, for this architecture and provide modules to dynamically detect the best strategy for a given scenario. Our results indicate that due to the performance characteristics of the underlying Lustre setup, one shuffle strategy may outperform another in different HPC environments, and our dynamic detection mechanism can deliver best performance based on the performance characteristics obtained during runtime of job execution. Through this design, we can achieve 44% performance benefit for shuffle-intensive workloads in leadership-class HPC systems. To the best of our knowledge, this is the first attempt to exploit performance characteristics of alternate shuffle strategies for YARN MapReduce with Lustre and RDMA.
Starting Page	291
Ending Page	300
File Size	371017
Page Count	10
File Format	PDF
ISBN	9781479986491
ISSN	15302075
DOI	10.1109/IPDPS.2015.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Yarn Servers Computer architecture File systems Bandwidth Big data Data analysis HPC Clusters MapReduce YARN Lustre RDMA
Content Type	Text
Resource Type	Article

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