ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads

Kalyanasundaram, Jayanth and Simmhan, Yogesh (2017) ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads. In: IEEE 24th International Conference on High Performance Computing Workshops (HiPCW), DEC 18-21, 2017, Jaipur, INDIA, pp. 203-212.

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Abstract

ARM processors have dominated the mobile device market in the last decade due to their favorable computing to energy ratio. In this age of Cloud data centers and Big Data analytics, the focus is increasingly on power efficient processing, rather than just high throughput computing. ARM's first commodity server-grade processor is the recent AMD A1100-series processor, based on a 64-bit ARM Cortex A57 architecture. In this paper, we study the performance and energy efficiency of a server based on this ARM64 CPU, relative to a comparable server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads. Specifically, we study these for Intel's HiBench suite of web, query and machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed setup, for data sizes up to 20GB files, 5M web pages and 500M tuples. Our results show that the ARM64 server's runtime performance is comparable to the x64 server for integer-based workloads like Sort and Hive queries, and only lags behind for floating-point intensive benchmarks like PageRank, when they do not exploit data parallelism adequately. We also see that the ARM64 server takes 1/3rd the energy, and has an Energy Delay Product (EDP) that is 50 - 71% lower than the x64 server. These results hold promise for ARM64 data centers hosting Big Data workloads to reduce their operational costs, while opening up opportunities for further analysis.

Item Type:	Conference Proceedings
Series.:	International Conference on High Performance Computing
Publisher:	IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Additional Information:	Copy right for the article belong to IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	04 Apr 2018 18:50
Last Modified:	04 Apr 2018 18:50
URI:	http://eprints.iisc.ac.in/id/eprint/59491

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