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SCAF: A Speculation-Aware Collaborative Dependence Analysis Framework [abstract] (PDF)
Sotiris Apostolakis, Ziyang Xu, Zujun Tan, Greg Chan, Simone Campanoni, and David I. August
Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), June 2020.
Awarded all top ACM Reproducibility Badges offered by the Artifact Evaluation Committee.

Program analysis determines the potential dataflow and control flow relationships among instructions so that compiler optimizations can respect these relationships to transform code correctly. Since many of these relationships rarely or never occur, speculative optimizations assert they do not exist while optimizing the code. To preserve correctness, speculative optimizations add validation checks to activate recovery code when these assertions prove untrue. This approach results in many missed opportunities because program analysis and thus other optimizations remain unaware of the full impact of these dynamically-enforced speculative assertions. To address this problem, this paper presents SCAF, a Speculation-aware Collaborative dependence Analysis Framework. SCAF learns of available speculative assertions via profiling, computes their full impact on memory dependence analysis, and makes this resulting information available for all code optimizations. SCAF is modular (adding new analysis modules is easy) and collaborative (modules cooperate to produce a result more precise than the confluence of all individual results). Relative to the best prior speculation-aware dependence analysis technique, by computing the full impact of speculation on memory dependence analysis, SCAF dramatically reduces the need for expensive-to-validate memory speculation in the hot loops of all 16 evaluated C/C++ SPEC benchmarks.