Scalable conditional induction variables (CIV) analysis

Cosmin E. Oancea, Lawrence Rauchwerger

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

9 Scopus citations


Subscripts using induction variables that cannot be expressed as a formula in terms of the enclosing-loop indices appear in the low-level implementation of common programming abstractions such as Alter, or stack operations and pose significant challenges to automatic parallelization. Because the complexity of such induction variables is often due to their conditional evaluation across the iteration space of loops we name them Conditional Induction Variables (CIV). This paper presents a flow-sensitive technique that summarizes both such CIV-based and affine subscripts to program level, using the same representation. Our technique requires no modifications of our dependence tests, which is agnostic to the original shape of the subscripts, and is more powerful than previously reported dependence tests that rely on the pairwise disambiguation of read-write references. We have implemented the CIV analysis in our parallelizing compiler and evaluated its impact on five Fortran benchmarks. We have found that that there are many important loops using CIV subscripts and that our analysis can lead to their scalable parallelization. This in turn has led to the parallelization of the benchmark programs they appear in.
Original languageEnglish (US)
Title of host publication2015 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages12
ISBN (Print)9781479981618
StatePublished - Feb 2015
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 the Danish Councilfor Strategic Research under contract number 10-092299(HIPERFIT), and by NSF awards CCF 0702765, CNS-0551685, CCF-0833199, CCF-1439145, CCF-1423111, CCF-0830753, IIS-0917266, by DOE awards DE-AC02-06CH11357,DE-NA0002376, B575363, by Samsung, IBM, Intel, and byAward KUS-C1-016-04, made by King Abdullah Universityof Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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