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Towards Energy Efficient Data Mining & Graph Processing

Faisal, S M
http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739

Abstract Details

2015, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Ever increasing energy cost is one of the most critical concerns for large scale deployments of data centers. As the demand for large scale data processing increases, it is paramount that energy efficiency is taken into account for designing architectures as well as algorithms for large scale data processing. While cost is a critical issue, it is not the only point of interest; Increased energy consumption has severe impact on the environment. Hence, it is important to pay close attention towards energy efficient data mining and graph processing algorithms that leverage architectural as well as algorithmic features to reduce energy consumption while serving respective purposes with a reduced carbon footprint. In this work, we take a close look at energy efficiency in the broad area of data mining and graph processing and approach the problem from multiple fronts. First, we take a pure software centric approach where we focus on developing frameworks that provide faster solutions to problems that are expensive otherwise and save energy thereby – following the race-to-halt phenomenon. Our proposed framework allows space efficient representation, scalable distributed processing and ease of programming for large, power law graphs. We also develop parallel, distributed implementations of a popular graph clustering algorithm, Regularized Markov Clus- tering (RMCL), on various distributed memory programming frameworks. Next we analyze commonly used data mining, multimedia and graph clustering algorithms to explore their energy profile and tolerance to random bit errors induced by low voltage computation. At the core of any research on energy efficient, low voltage computing is reliable error models for functional units at low voltage. We find that existing models lack sufficient detail and fail to capture the behavior in a realistic manner. Driven by the necessity, we propose a set of accurate, robust and realistic models for functional units’ behavior at low voltage. Finally, We take a hardware- software co-design approach where a combination of energy efficient hardware and energy conscious software cooperate with each other to execute jobs efficiently with respect to energy consumption. We propose a novel framework for energy efficient graph processing that identifies important edges in a graph and applies energy efficient computing across edges that are not important for the graph. In this dissertation we propose various solutions to improving energy efficiency of large scale data mining and graph processing applications. Our parallel framework provides scalability and efficiency while processing large graphs. Our error models provide estimation within 1-3% of comprehensive analog simulations and 5-17x higher accuracy compared to existing error models. Our energy efficient graph processing framework allows for processing of large, modern graphs while saving 3-30% in power consumption. All of our proposed techniques provide high quality output for various data mining and graph processing algorithms while saving significant amount of energy.
Srinivasan Parthasarathy (Advisor)
P. Sadayappan (Committee Member)
Radu Teodorescu (Committee Member)
243 p.
Computer Science
Energy Efficiency; Data Mining; Graph Processing; Energy Efficient Computing

Recommended Citations

Citations

  • Faisal, S. M. (2015). Towards Energy Efficient Data Mining & Graph Processing [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739

    APA Style (7th edition)

  • Faisal, S M. Towards Energy Efficient Data Mining & Graph Processing. 2015. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739.

    MLA Style (8th edition)

  • Faisal, S M. "Towards Energy Efficient Data Mining & Graph Processing." Doctoral dissertation, Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739

    Chicago Manual of Style (17th edition)

osu1440364739
1,052
© 2015, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.