WECPAR: List Ranking Algorithm and Relative Computational Power

H. M. El-Boghdadi
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Abstract

Reconfigurable models were shown to be very powerful in solving many problems faster than non reconfigurable models. WECPAR W(M,N,k) is an M × N reconfigurable model that has point-to-point reconfigurable interconnection with k wires between neighboring processors. This paper studies several aspects of WECPAR. We first solve the list ranking problem on WECPAR. Some of the results obtained show that ranking one element in a list of N elements can be solved on W(N,N,N) WECPAR in O(1) time. Also, on W(N,N,k), ranking a list L(N) of N elements can be done in O((log N)([logk+1])) time. To transfer a large body of algorithms to work on WECPAR and to assess its relative computational power, several simulations algorithms are introduced between WECPAR and well-known models such as PRAM and RMBM. Simulations algorithms show that a PRIORITY CRCW PRAM of N processors and S shared memory locations can be simulated by an W(S, N, k) WECPAR in O([logk+1 N]+[log Sk+1]) time. Also, we show that a PRIORITY CRCW Basic-RMBM(P,B), of P processors and B buses can be simulated by an W(B, P+B, k) WECPAR in O([logk+1 (P+B)]) time. This has the effect of migrating a large number of algorithms to work directly on WECPAR with the simulation overhead.
WECPAR:列表排序算法和相对计算能力
可重构模型在解决许多问题方面比不可重构模型更有效。WECPAR W(M,N,k)是一种M × N可重构模型,在相邻处理器之间通过k条线实现点对点可重构互连。本文对WECPAR的几个方面进行了研究。我们首先解决了WECPAR上的列表排序问题。得到的一些结果表明,在W(N,N,N)个WECPAR上,在O(1)时间内可以解决N个元素列表中一个元素的排序问题。同样,在W(N,N,k)上,对包含N个元素的列表L(N)进行排序可以在O((log N)([log +1]))时间内完成。为了将大量的算法转移到WECPAR上并评估其相对的计算能力,在WECPAR和众所周知的模型(如PRAM和RMBM)之间引入了几种模拟算法。仿真算法表明,用W(S, N, k) WECPAR可以在O([log +1 N]+[log Sk+1])时间内模拟出N个处理器和S个共享内存位置的优先级CRCW PRAM。此外,我们还证明了P个处理器和B个总线的优先级CRCW Basic-RMBM(P,B),可以用W(B, P+B, k) WECPAR在O([logk+1 (P+B)])时间内模拟。这样做的效果是将大量算法迁移到直接在WECPAR上工作,并增加了模拟开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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