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A study on popular auto‐parallelization frameworks

2019; Wiley; Volume: 31; Issue: 17 Linguagem: Inglês

10.1002/cpe.5168

1532-0634

S. Prema, Rupesh Nasre, R. Jehadeesan, B. K. Panigrahi,

Advanced Data Storage Technologies

Summary We study five popular auto‐parallelization frameworks (Cetus, Par4all, Rose, ICC, and Pluto) and compare them qualitatively as well as quantitatively. All the frameworks primarily deal with loop parallelization but differ in the techniques used to identify parallelization opportunities. Due to this variance, various aspects, such as certain loop transformations, are supported only in a few frameworks. The frameworks exhibit varying abilities in handling loop‐carried dependence and, therefore, achieve different amounts of speedup on widely used PolyBench and NAS parallel benchmarks. In particular, Intel C Compiler (ICC) fares as an overall good parallelizer. Our study also highlights the need for more sophisticated analyses, user‐driven parallelization, and meta‐auto‐parallelizer that provides combined benefits of various frameworks.