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Improving Polyhedral-Based Optimizations with Dynamic Coordinate Descent

SessionResearch, ACM SRC, and Doctoral Showcase Posters

DescriptionPolyhedral optimizations have been a cornerstone of kernel optimization for many years. These techniques use a geometric model of loop iterations to enable transformations like tiling, fusion, and fission. The elegance of this approach lies in its ability to produce highly efficient code through fully static optimizations. However, modern kernel schedulers typically avoid the polyhedral model, opting instead for dynamic sampling techniques, such as evolutionary searches, to generate efficient code. The polyhedral model is often bypassed because, being entirely static, it struggles to adapt to the fine details of hardware. In this work, we demonstrate that it is possible to overcome this limitation by combining the polyhedral model with a post-optimization phase based on dynamic coordinate descent, which uses minimal sampling while still achieving excellent performance.

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