NDLI: Using graph-based program characterization for predictive modeling

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Using graph-based program characterization for predictive modeling

Content Provider	ACM Digital Library
Author	Park, Eunjung Cavazos, John Alvarez, Marco A.
Abstract	Using machine learning has proven effective at choosing the right set of optimizations for a particular program. For machine learning techniques to be most effective, compiler writers have to develop expressive means of characterizing the program being optimized. The current state-of-the-art techniques for characterizing programs include using a fixed-length feature vector of either source code features extracted during compile time or performance counters collected when running the program. For the problem of identifying optimizations to apply, models constructed using performance counter characterizations of a program have been shown to outperform models constructed using source code features. However, collecting performance counters requires running the program multiple times, and this "dynamic" method of characterizing programs can be specific to inputs of the program. It would be preferable to have a method of characterizing programs that is as expressive as performance counter features, but that is "static" like source code features and therefore does not require running the program. In this paper, we introduce a novel way of characterizing programs using a graph-based characterization, which uses the program's intermediate representation and an adapted learning algorithm to predict good optimization sequences. To evaluate different characterization techniques, we focus on loop-intensive programs and construct prediction models that drive polyhedral optimizations, such as auto-parallelism and various loop transformation. We show that our graph-based characterization technique outperforms three current state-of-the-art characterization techniques found in the literature. By using the sequences predicted to be the best by our graph-based model, we achieved up to 73% of the speedup achievable in our search space for a particular platform, whereas we could only achieve up to 59% by other state-of-the-art techniques we evaluated.
Starting Page	196
Ending Page	206
Page Count	11
File Format	PDF
ISBN	9781450312066
DOI	10.1145/2259016.2259042
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-03-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Machine learning Compiler optimization Graph-based program characterization Support vector machine Iterative compilation
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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