Abstract
We introduce a framework for the analysis of memory reference sets addressed by induction variables without closed forms. This framework relies on a new data structure, the Value Evolution Graph (VEG), which models the global flow of scalar and array values within a program. We describe the application of our framework to array data-flow analysis, privatization, and dependence analysis. This results in the automatic parallelization of loops that contain arrays indexed by induction variables without closed forms. We implemented this framework in the Polaris research compiler. We present experimental results on a set of codes from the PERFECT, SPEC, and NCSA benchmark suites.
Research supported in part by NSF CAREER Award CCR-9734471, NSF Grant ACI-9872126, NSF Grant EIA-0103742, NSF Grant ACI-0326350, NSF Grant ACI-0113971, DOE ASCI ASAP Level 2 Grant B347886.
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Rus, S., Zhang, D., Rauchwerger, L. (2005). Automatic Parallelization Using the Value Evolution Graph. In: Eigenmann, R., Li, Z., Midkiff, S.P. (eds) Languages and Compilers for High Performance Computing. LCPC 2004. Lecture Notes in Computer Science, vol 3602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11532378_27
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DOI: https://doi.org/10.1007/11532378_27
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