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Hybrid analysis of memory references and its application to automatic parallelization

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dc.contributor.advisor Rauchwerger, Lawrence en_US
dc.creator Rus, Silvius Vasile en_US
dc.date.accessioned 2010-01-15T00:01:31Z en_US
dc.date.accessioned 2010-01-16T02:10:18Z
dc.date.available 2010-01-15T00:01:31Z en_US
dc.date.available 2010-01-16T02:10:18Z
dc.date.created 2006-12 en_US
dc.date.issued 2009-05-15 en_US
dc.identifier.uri http://hdl.handle.net/1969.1/ETD-TAMU-1076
dc.description.abstract Executing sequential code in parallel on a multithreaded machine has been an elusive goal of the academic and industrial research communities for many years. It has recently become more important due to the widespread introduction of multicores in PCs. Automatic multithreading has not been achieved because classic, static compiler analysis was not powerful enough and program behavior was found to be, in many cases, input dependent. Speculative thread level parallelization was a welcome avenue for advancing parallelization coverage but its performance was not always optimal due to the sometimes unnecessary overhead of checking every dynamic memory reference. In this dissertation we introduce a novel analysis technique, Hybrid Analysis, which unifies static and dynamic memory reference techniques into a seamless compiler framework which extracts almost maximum available parallelism from scientific codes and incurs close to the minimum necessary run time overhead. We present how to extract maximum information from the quantities that could not be sufficiently analyzed through static compiler methods, and how to generate sufficient conditions which, when evaluated dynamically, can validate optimizations. Our techniques have been fully implemented in the Polaris compiler and resulted in whole program speedups on a large number of industry standard benchmark applications. en_US
dc.format.medium electronic en_US
dc.format.mimetype application/pdf en_US
dc.language.iso en_US en_US
dc.subject Compiler en_US
dc.subject Optimization en_US
dc.subject Hybrid Analysis en_US
dc.subject Program Representation en_US
dc.title Hybrid analysis of memory references and its application to automatic parallelization en_US
dc.type Book en
dc.type Thesis en
thesis.degree.department Computer Science en_US
thesis.degree.discipline Computer Science en_US
thesis.degree.grantor Texas A&M University en_US
thesis.degree.name Doctor of Philosophy en_US
thesis.degree.level Doctoral en_US
dc.contributor.committeeMember Amato, Nancy en_US
dc.contributor.committeeMember Reddy, Narasimha en_US
dc.contributor.committeeMember Sarin, Vivek en_US
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US


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