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Section: New Results

Polyhedral Compiler White-Boxing

Participants : Cédric Bastoul, Lénaïc Bagnères, Oleksandr Zinenko, Stéphane Huot.

While compilers offer a fair trade-off between productivity and executable performance in single-threaded execution, their optimizations remain fragile when addressing compute-intensive code for parallel architectures with deep memory hierarchies. Moreover, these optimizations operate as black boxes, impenetrable for the user, leaving them with no alternative to time-consuming and error-prone manual optimization in cases where an imprecise cost model or a weak analysis resulted in a bad optimization decision. To address this issue, we researched and designed a technique allowing to automatically translate an arbitrary polyhedral optimization, used internally by loop-level optimization frameworks of several modern compilers, into a sequence of comprehensible syntactic transformations as long as this optimization focuses on scheduling loop iterations. With our approach, we open the black box of the polyhedral frameworks enabling users to examine, refine, replay and even design complex optimizations semi-automatically in partnership with the compiler.

This research started in 2014 and we found the first solution in 2015. It has been conducted as a joint work between teams in compiler technologies (CAMUS and Inria Saclay's POSTALE team) and teams in interaction (EX-SITU at Inria Saclay and MJOLNIR at Inria Lille). The first paper on this has been accepted in 2015 to be presented in one of the top conferences on optimization techniques: CGO 2016 [15] . Subsequent work and a first release of the tool implementing the technique is planned during 2016.