NDLI: Predicting data structures for energy efficient computing

Content Provider	IEEE Xplore Digital Library
Author	Michanan, J. Dewri, R. Rutherford, M.J.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Denver, Denver, CO, USA (Michanan, J.; Dewri, R.; Rutherford, M.J.)
Abstract	Dynamic data structures in software applications have been shown to have a large impact on system performance. In this paper, we explore energy saving opportunities of interface-based dynamic data structures. Our results suggest that opportunities do exist in the C5 Collection, at least 16.95% and up to 97.50%. We propose an architecture for building adaptive green data structures by applying machine learning tools to build a model for predicting energy efficient data structures. Our neural network model can classify energy efficient data structures based on features such as the number of elements, frequency of operations, interface and set/bag semantics. The 10-fold cross validation results show 96.01% accuracy on the training data and 95.80% on the training validation data. Our n-gram model can accurately predict the most energy efficient data structure sequence in 19 simulated and real-world programs-on average, with more than 50% accuracy and up to 98% using a bigram predictor.
Starting Page	1
Ending Page	8
File Size	838911
Page Count	8
File Format	PDF
e-ISBN	9781509001729
DOI	10.1109/IGCC.2015.7393698
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	N-Gram Energy consumption Adaptation models C5 Collection Bigram Predictive models Data structures Machine Learning Neural Network Software Adaptation Energy Green products Efficiency Green Data Structure Energy efficiency Software Performance Power
Content Type	Text
Resource Type	Article

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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
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