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arXiv · 1508.01847

Big Data Analytics on Traditional HPC Infrastructure Using Two-Level Storage

Pengfei Xuan·Jeffrey Denton·Rong Ge·Pradip K. Srimani·Feng Luo
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Abstract

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.

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Pengfei Xuan, Jeffrey Denton, Rong Ge, Pradip K. Srimani, Feng Luo. 2015-08-08. Big Data Analytics on Traditional HPC Infrastructure Using Two-Level Storage. https://doi.org/10.1145/2831244.2831253

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