基于约束网络的存储器布局优化方法
A Constraint Network Based Approach to Memory Layout Optimization
摘要：
虽然基于循环重构的代码优化在过去对于数组密集型应用已经是成功的，但是它具有若干问题，诸如检查依赖关系的要求（合法性问题）和不加区别地转换循环体内的所有数组引用（虽然一些引用可以从转换中受益，但是其他引用可能不受益）。因此，数据转换，即，已经提出了修改阵列数据的存储器布局而不是循环结构的变换。与数据转换相关的问题之一是，很难为整个程序（而不仅仅是单个循环）选择可接受的数组内存布局。本文将阵列的存储器布局问题描述为一个约束网络，并系统地探讨了几种求解方法。我们的实验提供了强有力的支持，有利于采用约束处理，并指出未来的研究方向。
英文摘要：
While loop restructuring based code optimization for array intensive applications has been successful in the past, it has several problems such as the requirement of checking dependences (legality issues) and transformation of all of the array references within the loop body indiscriminately (while some of the references can benefit from the transformation, others may not). As a result, data transformations, i.e., transformations that modify memory layout of array data instead of loop structure have been proposed. One of the problems associated with data transformations is the difficulty of selecting a memory layout for an array that is acceptable to the entire program (not just to a single loop). In this paper, we formulate the problem of determining the memory layouts of arrays as a constraint network, and explore several methods of solution in a systematic way. Our experiments provide strong support in favor of employing constraint processing, and point out future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
