Logical inference techniques for loop parallelization

Cosmin E. Oancea, Lawrence Rauchwerger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


This paper presents a fully automatic approach to loop parallelization that integrates the use of static and run-time analysis and thus overcomes many known difficulties such as nonlinear and indirect array indexing and complex control flow. Our hybrid analysis framework validates the parallelization transformation by verifying the independence of the loop's memory references. To this end it represents array references using the USR (uniform set representation) language and expresses the independence condition as an equation, S = Ø, where S is a set expression representing array indexes. Using a language instead of an array-abstraction representation for S results in a smaller number of conservative approximations but exhibits a potentially-high runtime cost. To alleviate this cost we introduce a language translation F from the USR set-expression language to an equally rich language of predicates (F(S) ⇒ S = Ø). Loop parallelization is then validated using a novel logic inference algorithm that factorizes the obtained complex predicates (F(S)) into a sequence of sufficient-independence conditions that are evaluated first statically and, when needed, dynamically, in increasing order of their estimated complexities. We evaluate our automated solution on 26 benchmarks from PERFECTCLUB and SPEC suites and show that our approach is effective in parallelizing large, complex loops and obtains much better full program speedups than the Intel and IBM Fortran compilers. Copyright © 2012 ACM.
Original languageEnglish (US)
Title of host publicationProceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation - PLDI '12
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Print)9781450312059
StatePublished - 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This work was supported in part by NSF awards CRI-0551685,CCF-0833199, CCF-0830753, IIS-0917266, NSF/DNDO award2008-DN-077-ARI018-02, by the DOE NNSA PSAAP grant DEFC52-08NA28616, IBM, Intel, Oracle/Sun and Award KUS-C1-016-04, made by King Abdullah University of Science and Technology(KAUST). Since November 2011 Cosmin Oancea was supportedby Danish Strategic Research Council, for the HIPERFITresearch center under contract 10-092299.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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