A general approach to solving arbitrarily large problems in a fixed size systolic array

Henry Y. H. Chuang, L. Chen
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引用次数: 2

Abstract

Solving large problems in a small systolic array is considered difficult because the data arrays that pipeline through the array have to meet at the right place at the right time. Thus, partitioning and scheduling of data arrays for systolic computation is generally performed ad hoc. A general partitioning and scheduling method for solving large problems in a small systolic array is presented. Given a systolic array and its algorithm, a scheduling program that schedules the data blocks resulting from partitioning the data arrays can be obtained in a straightforward manner from the algorithm. The method, which can be easily mechanized, is applicable to loop algorithms and systolic arrays of various structures and shapes with or without local memory. Array emulation is an effective approach to solve large problems in a small processor array efficiently. Various array emulation methods are discussed, and the partitioning and scheduling method presented is shown to be useful for array emulation also.<>
在固定大小的收缩数组中求解任意大问题的一般方法
在一个小的收缩数组中解决大的问题被认为是困难的,因为流经数组的数据数组必须在正确的时间在正确的位置相遇。因此,收缩计算的数据数组的分区和调度通常是临时执行的。提出了一种在小型收缩阵列中求解大型问题的通用分区和调度方法。给定收缩数组及其算法,可以从该算法中以直接的方式获得调度由数据数组分区产生的数据块的调度程序。该方法易于机械化,适用于具有或不具有局部存储器的各种结构和形状的循环算法和收缩阵列。阵列仿真是在小型处理器阵列中高效解决大型问题的有效途径。讨论了阵列仿真的各种方法,提出了分区和调度方法,对阵列仿真也很有用
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