ParaSCAN: A Static Profiler to Help Parallelization Upadhyaya, Ganesha Rajan, Hridesh Rajan, Hridesh Sondag, Tyler
dc.contributor.department Computer Science 2018-02-14T01:49:28.000 2020-06-30T01:56:59Z 2020-06-30T01:56:59Z 2014-05-13
dc.description.abstract <p>Parallelizing software often starts by profiling to identify program paths that are worth parallelizing. Static profiling techniques, e.g. hot paths, can be used to identify parallelism opportunities for programs that lack representative inputs and in situations where dynamic techniques aren't applicable, e.g. parallelizing compilers and refactoring tools. Existing static techniques for identification of hot paths rely on path frequencies. Relying on path frequencies alone isn't sufficient for identifying parallelism opportunities. We propose a novel automated approach for static profiling that combines both path frequencies and computational weight of the paths. We apply our technique called ParaSCAN to parallelism recommendation, where it is highly effective. Our results demonstrate that ParaSCAN's recommendations cover all the parallelism manually identified by experts with 85% accuracy and in some cases also identifies parallelism missed by the experts.</p>
dc.description.comments <p>Copyright © 2014, Ganesha Upadhyaya, Tyler Sondag, and Hridesh Rajan.</p>
dc.identifier archive/
dc.identifier.articleid 1360
dc.identifier.contextkey 5696461
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath cs_techreports/361
dc.source.bitstream archive/|||Fri Jan 14 23:47:25 UTC 2022
dc.subject.disciplines Computer and Systems Architecture
dc.subject.keywords parallelism discovery and planning
dc.subject.keywords software evolution
dc.title ParaSCAN: A Static Profiler to Help Parallelization
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456
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