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2015 International Conference on Parallel Architecture and Compilation (PACT) (2015)
San Francisco, CA, USA
Oct. 18, 2015 to Oct. 21, 2015
ISSN: 1089-795X
ISBN: 978-1-4673-9524-3
pp: 490-491
The polyhedral model is a powerful algebraic framework that has enabled significant advances in analysis and transformation of sequential affine (sub)programs, relative to traditional AST-based approaches. However, given the rapid growth of parallel software, there is a need for increased attention to using polyhedral compilation techniques to analyze and transform explicitly parallel programs. In our PACT'15 paper titled "Polyhedral Optimizations of Explicitly Parallel Programs" [1, 2], we addressed the problem of analyzing and transforming programs with explicit parallelism that satisfy the serial-elision property, i.e., the property that removal of all parallel constructs results in a sequential program that is a valid (albeit inefficient) implementation of the parallel program semantics.In this poster, we address the problem of analyzing and transforming more general OpenMP programs that do not satisfy the serial-elision property. Our contributions include the following: 1) An extension of the polyhedral model to represent input OpenMP programs, 2) Formalization of May Happen in Parallel (MHP) and Happens before (HB) relations in the extended model, 3) An approach for static detection of data races in OpenMP programs by generating race constraints that can be solved by an SMT solver such as Z3, and 4) An approach for transforming OpenMP programs.
Schedules, Parallel processing, Instruction sets, Parallel architectures, Analytical models, Semantics

P. Chatarasi and V. Sarkar, "Extending Polyhedral Model for Analysis and Transformation of OpenMP Programs," 2015 International Conference on Parallel Architecture and Compilation (PACT), San Francisco, CA, USA, 2015, pp. 490-491.
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