Show simple item record

dc.contributor.advisorQasem, Apan
dc.contributor.advisorEkstrand, Michael
dc.contributor.authorSaha, Shuvabrata ( )
dc.date.accessioned2016-06-28T19:22:27Z
dc.date.available2016-06-28T19:22:27Z
dc.date.issued2016-05
dc.identifier.citationSaha, S. (2016). A multi-objective autotuning framework for the java virtual machine (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/6096
dc.description.abstractDue to inherent limitations in performance, Java was not considered a suitable platform for for scalable high-performance computing (HPC) for a long time. The scenario is changing because of the development of frameworks like Hadoop, Spark and Fast-MPJ. In spite of the increase in usage, achieving high performance with Java is not trivial. High performance in Java relies on libraries providing explicit threads or relying on runnable-like interfaces for distributed programming. In this thesis, we develop an autotuning framework for JVM that manages multiple objective functions including execution time, power consumption, energy and perfomance-per-watt. The framework searches the combined space of JIT optimization sequences and different classes of JVM runtime parameters. To discover good configurations more quickly, the framework implements novel heuristic search algorithms. To reduce the size of the search space machine-learning based pruning techniques are used. Evaluation on recommender system workloads show that significant improvements in both performance and power can be gained by fine-tuning JVM runitme parameters.
dc.formatText
dc.format.extent64 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectMulti-objective
dc.subjectAutotuning
dc.subject.lcshJava virtual machineen_US
dc.subject.lcshJava (Computer program language)en_US
dc.subject.lcshVirtual computer systemsen_US
dc.titleA Multi-objective Autotuning Framework For The Java Virtual Machine
txstate.documenttypeThesis
dc.contributor.committeeMemberChen, Xiao
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorTexas State Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
txstate.departmentComputer Science


Download

Thumbnail

This item appears in the following Collection(s)

Show simple item record