A Collaborative Dependence Analysis Framework [abstract] (ACM DL, PDF)
Nick P. Johnson, Jordan Fix, Taewook Oh, Stephen R. Beard, Thomas B. Jablin, and David I. August
Proceedings of the 2017 International Symposium on Code Generation and Optimization (CGO), February 2017.
Highest ranked paper in double-blind review process.

Compiler optimizations discover facts about program behavior by querying static analysis. However, developing or extending precise analysis is difficult. Some prior works implement analysis with a single algorithm, but the algorithm becomes more complex as it is extended for greater precision. Other works achieve modularity by implementing several simple algorithms and trivially composing them to report the best result from among them. Such a modular approach has limited precision because it employs only one algorithm in response to one query, without synergy between algorithms. This paper presents a framework for dependence analysis algorithms to collaborate and achieve precision greater than the trivial combination of those algorithms. With this framework, developers can achieve the high precision of complex analysis algorithms through collaboration of simple and orthogonal algorithms, without sacrificing the ease of implementation of the modular approach. Results demonstrate that collaboration of simple analyses enables advanced compiler optimizations.