An Effective Fusion and Tile Size Model for Optimizing Image Processing Pipelines

Effective models for fusion of loop nests continue to remain a challenge in both general-purpose and domain-specific language (DSL) compilers. The difficulty often arises from the combinatorial explosion of grouping choices and their interaction with parallelism and locality. This paper presents a new fusion algorithm for high-performance domain-specific compilers for image processing pipelines. The fusion algorithm is driven by dynamic programming and explores spaces of fusion possibilities not covered by previous approaches, and is driven by a cost function more concrete and precise in capturing optimization criteria than prior approaches. The fusion model is particularly tailored to the transformation and optimization sequence applied by PolyMage and Halide, two recent DSLs for image processing pipelines. Our model-driven technique when implemented in PolyMage provides significant improvements (up to 4.32 x) over PolyMage's approach (which uses auto-tuning to aid its model), and over Halide's automatic approach (by up to 2.46 x) on two state-of-the-art shared-memory multicore architectures