高效嵌套递归计算的深度融合

A. Shaikhha
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引用次数: 2

摘要

嵌套递归计算的性能瓶颈之一是在不同递归级别上创建的中间集合。现有的技术,如垂直和水平环路融合,并没有消除这种中间分配。本文提出了一种深度融合技术,用于有效地编译嵌套的递归计算。编译框架的输入是一个高级函数程序,它可以表示平面和嵌套集合(如列表、集合、包和地图)上的计算。中间集合分三个级别删除。首先,通过使用目标传递风格技术利用就地更新,将不可变集合转换为可变集合。其次,深度融合使内部递归层能够重用外部递归层的目标以进行就地更新。第三,深度融合消除了在不同递归深度分配微小中间集合的需要。实验表明,深度融合可以提高嵌套列表和嵌套映射的嵌套递归性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Fusion for Efficient Nested Recursive Computations
One of the performance bottlenecks of nested recursive computations is the intermediate collections created at different levels of recursion. The existing techniques such as vertical and horizontal loop fusion do not remove such intermediate allocations. This paper proposes deep fusion, a technique for the efficient compilation of nested recursive computation over collections. The input to our compilation framework is a high-level functional program that can represent computations on flat and nested collections such as lists, sets, bags, and maps. The intermediate collections are removed in three levels. First, the immutable collections are translated into mutable ones by leveraging in-place updates using the destination-passing style technique. Second, deep fusion enables the inner level of recursion to reuse the destinations of the outer levels for in-place updates. Third, deep fusion removes the need to allocate tiny intermediate collections at different depths of recursion. Our experiments show that deep fusion can improve the performance of nested recursion over nested lists and maps.
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