NDLI: Power Efficient MapReduce Workload Acceleration Using Integrated-GPU

Content Provider	IEEE Xplore Digital Library
Author	SungYe Kim Bottleson, J. Jingyi Jin Bindu, P. Sakhare, S.C. Spisak, J.S.
Copyright Year	2015
Description	Author affiliation: Intel Corp., Folsom, CA, USA (SungYe Kim; Bottleson, J.; Jingyi Jin; Bindu, P.; Sakhare, S.C.; Spisak, J.S.)
Abstract	With the pervasiveness of MapReduce - one of the most prominent programming models for data parallelism in Apache Hadoop-, many researchers and developers have spent tremendous effort attempting to boost the computational speed and energy efficiency of MapReduce-based big data processing. However, the scalable and fault-tolerant nature of MapReduce introduces additional costs in disk IO and data transfer, caused by streaming intermediate outputs to disk. In light of these issues, many interesting research projects have been initiated with the goal of improving the compute speed and power efficiency of compute-intensive cloud computing workloads, several with the addition of discrete GPUs. In this work, we present a modified MapReduce approach focused on the iterative clustering algorithms in the Apache Mahout machine learning library that leverage the acceleration potential of the Intel integrated GPU in a multi-node cluster environment. The accelerated framework shows varying levels of speed-up (≈45x for Map tasks-only, ≈4.37x for the entire K-means clustering) as evaluated using the HiBench benchmark suite. Based on various experiments and in-depth analysis, we find that utilizing the integrated GPU via OpenCL offers significant performance and power efficiency gains over the original CPU based approach. Further analysis is also done to understand the correlations between compute, IO and power efficiency. As such, our results show that embracing the integrated GPU in the Hadoop MapReduce framework represents a promising advance in adding cost and energy efficient compute parallelism to a data parallel multinode environment.
Starting Page	162
Ending Page	169
File Size	1497543
Page Count	8
File Format	PDF
ISBN	9781479981281
DOI	10.1109/BigDataService.2015.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Java Power demand Graphics processing units Hadoop Performance gain Big Data Machine Learning Optimization GPGPU Integrated Graphics Mahout OpenCL Acceleration Kernel
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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