Publication Date

Spring 5-13-2014

Technical Report Number



Computer Applications, Software


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.


Copyright © 2014, Ganesha Upadhyaya, Tyler Sondag, and Hridesh Rajan.