Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques (2009)
Raleigh, North Carolina, USA
Sept. 12, 2009 to Sept. 16, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PACT.2009.18
Optimizing compilers strive to construct efficient executables by applying sequences of transformations. Additional transformations are constantly being devised, with various mutual interactions among them, thereby exacerbating the notoriously difficult phase-ordering problem --- that of deciding which transformations to apply and in which order. Fortunately, new infrastructures such as the polyhedral compilation framework host a variety of transformations, facilitating the efficient exploration and configuration of multiple transformation sequences. Many powerful optimizations, however, remain external to the polyhedral framework, with potential mutual interactions that need to be considered. In this paper we examine the interactions between loop transformations of the polyhedral compilation framework and subsequent vectorization optimizations targeting fine-grain SIMD data-level parallelism. Automatic vectorization involves many low-level, target-specific considerations and transformations, which currently exclude it from being part of the polyhedral framework. In order to consider potential interactions among polyhedral loop transformations and vectorization, we first model the performance impact of the different loop transformations and vectorization strategies, and then show how this cost model can be integrated seamlessly into the polyhedral representation. This predictive modelling then facilitates efficient exploration and educated decision making on how to best apply various polyhedral loop transformations while considering the subsequent effects of different vectorization schemes. Our work demonstrates the feasibility and benefit of tuning the polyhedral model in the context of vectorization. Experimental results confirm that our model has accurate predictions, providing speedups of over 2 on average over traditional innermost-loop vectorization on PowerPC970 and Cell-SPU SIMD platforms.
Ira Rosen, Ayal Zaks, Albert Cohen, Konrad Trifunovic, Dorit Nuzman, "Polyhedral-Model Guided Loop-Nest Auto-Vectorization", Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, vol. 00, no. , pp. 327-337, 2009, doi:10.1109/PACT.2009.18