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A Static Data Dependence Analysis Approach for Software Pipelining

机译:用于软件流水线的静态数据依赖分析方法

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This paper introduces a new static data dependence constraint, called dependence difference inequality, which can deal with coupled subscripts for multi-dimensional array references. Unlike direction vectors, dependence difference inequalities are related to not only the iteration space for a loop program but also the operation distance between two operations. They are more strict than other methods, and can act as additional constraints to each variable in a linear system on their own or with others. As a result, the solution space for a linear system can be compressed heavily. So long as dependence difference inequalities do not satisfy simultaneously, the loop can be software-pipelined with any initiation interval even if there exists a data dependence between two operations. Meanwhile, by replacing direction vectors with dependence difference inequalities some conservative estimations made by other traditional data dependence analysis approaches can be eliminated.
机译:本文介绍了一种新的静态数据依赖约束,称为依赖差不等式,它可以处理多维数组引用的耦合下标。与方向向量不同,依赖差不等式不仅与循环程序的迭代空间有关,而且与两个操作之间的操作距离有关。它们比其他方法更严格,并且可以单独或与其他系统一起充当线性系统中每个变量的附加约束。结果,可以大大压缩线性系统的解空间。只要依赖关系差不等式不能同时满足,即使两个操作之间存在数据依赖关系,也可以使用任何启动间隔对循环进行软件流水线处理。同时,通过将方向矢量替换为相关性差异不等式,可以消除其他传统数据相关性分析方法所做的一些保守估计。

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