A Pipeline Pattern Detection Technique in Polly

Abstract

The polyhedral model has repeatedly shown how it facilitates various loop transformations, including loop parallelization, loop tiling, and software pipelining. However, parallelism is almost exclusively exploited on a per-loop basis without much work on detecting cross-loop parallelization opportunities. While many problems can be scheduled such that loop dimensions are dependence-free, the resulting loop parallelism does not necessarily maximize concurrent execution, especially not for unbalanced problems. In this work, we introduce a polyhedral-model-based analysis and scheduling algorithm that exposes and utilizes cross-loop parallelization through tasking. This work exploits pipeline patterns between iterations in different loop nests, and it is well suited to handle imbalanced iterations. Our LLVM/Polly-based prototype performs schedule modifications and code generation targeting a minimal, language agnostic tasking layer. We present results using an implementation of this API with the OpenMP task construct. For different computation patterns, we achieved speed-ups of up to 3.5 × on a quad-core processor while LLVM/Polly alone fails to exploit the parallelism.