Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/13447
Title: Machine learning based approaches to predict and optimize hot methods
Researcher: Sandra Johnson
Guide(s): Valli, S
Keywords: Machine learning algorithm, support vector machine, Low Level Virtual Machine
Upload Date: 28-Nov-2013
University: Anna University
Completed Date: 
Abstract: Just-in-time compilers in a virtual machine environment generate machine code for a method in a program, when the method is first called for execution, incurring a code generation overhead. Dynamic optimizations overcome this limitation by optimizing the generated code during runtime with the profile collected from the current run of the program. This research work aims at developing a novel selective optimization technique, to serve as an effective alternative to the existing profile-based selective optimization of the input program. The technique uses the machine learning algorithm, the Support Vector Machine (SVM) in the construction of a heuristic, to predict the target sequences in a program, called hot methods for selective optimization. Two independent predictive models, one for predicting the frequently called, and another for the long running hot methods in a program, are developed. The new knock-out algorithm developed to perform feature reduction is an extension of the classical sequential backward elimination process, coupled with a new knock-out scheme to systematically eliminate one or more features at every iteration. The results obtained on the program execution time are compared against Low Level Virtual Machine s (LLVM) default optimization heuristics. The performance of the relearning predictive models on different combinations of SPEC, UTDSP and Mediabench benchmarks is very impressive, with an improvement of 10% and 21% respectively for the frequently called, and the long running hot methods, over the systems without relearning. The results of relearning confirm the effective predictability of hot methods by the machine learning based models. All these observations indicate that the technique of machine learning based hot method prediction is an effective selective optimization option, in any embedded system application running a virtual machine. newline newline newline
Pagination: xix, 146
URI: http://hdl.handle.net/10603/13447
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File49.49 kBAdobe PDFView/Open
02_certificates.pdf683.75 kBAdobe PDFView/Open
03_abstract.pdf17.62 kBAdobe PDFView/Open
04_acknowledgement.pdf14.09 kBAdobe PDFView/Open
05_contents.pdf33.89 kBAdobe PDFView/Open
06_chapter 1.pdf25.82 kBAdobe PDFView/Open
07_chapter 2.pdf219.15 kBAdobe PDFView/Open
08_chapter 3.pdf105.57 kBAdobe PDFView/Open
09_chapter 4.pdf52.05 kBAdobe PDFView/Open
10_chapter 5.pdf534.48 kBAdobe PDFView/Open
11_chapter 6.pdf104.35 kBAdobe PDFView/Open
12_chapter 7.pdf323.94 kBAdobe PDFView/Open
13_chapter 8.pdf303.57 kBAdobe PDFView/Open
14_chapter 9.pdf20.41 kBAdobe PDFView/Open
15_references.pdf56.19 kBAdobe PDFView/Open
16_publications.pdf13.68 kBAdobe PDFView/Open
17_vitae.pdf12.57 kBAdobe PDFView/Open


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