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 article 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 it is also 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×) over PolyMage’s approach (which uses auto-tuning to aid its model) and over Halide’s automatic approach (by up to 2.46×) on two state-of-the-art shared-memory multicore architectures.

中文翻译：

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PolyMage 的有效融合和平铺尺寸模型

在通用和领域特定语言 (DSL) 编译器中，用于融合循环嵌套的有效模型仍然是一个挑战。困难通常来自分组选择的组合爆炸以及它们与并行性和局部性的相互作用。本文介绍了一种用于图像处理管道的高性能特定领域编译器的新融合算法。融合算法由动态规划驱动，探索先前方法未涵盖的融合可能性空间，并且它还由成本函数驱动，该成本函数在捕获优化标准方面比先前方法更具体和精确。融合模型特别针对 PolyMage 和 Halide 这两个最近用于图像处理管道的 DSL 应用的转换和优化序列。