Skip to main navigation Skip to search Skip to main content

An efficient parallel algorithm for term matching

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Term matching is a compute-intensive problem that often arises in symbolic manipulation systems like term rewriting, and in functional and equational programming. A parallel algorithm for term matching on the CREW PRAM model was recently described by Dwork, Kanellakis and Stockmeyer. This algorithm requires O(n2) processors and takes either O(logn) or O(log2n) time. In this paper we describe a new parallel algorithm that performs term matching in O(log2n) time using O(n) processors. In our algorithm, we represent the two terms as labeled directed trees. We then construct equivalence classes of nodes in these two trees such that two nodes are in the same class iff they have the same sequence of edge-labels on the path to their respective roots. This is the basis of our parallel algorithm for term matching.

Original languageEnglish
Title of host publicationFoundations of Software Technology and Theoretical Computer Science - 6th Conference, Proceeding
EditorsKesav V. Nori
PublisherSpringer Verlag
Pages504-518
Number of pages15
ISBN (Print)9783540171799
DOIs
StatePublished - 1986
Event6th Conference on Foundations of Software Technology and Theoretical Computer Science, FST and TCS 1986 - New Delhi, India
Duration: Dec 18 1986Dec 20 1986

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume241 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Conference on Foundations of Software Technology and Theoretical Computer Science, FST and TCS 1986
Country/TerritoryIndia
CityNew Delhi
Period12/18/8612/20/86

Fingerprint

Dive into the research topics of 'An efficient parallel algorithm for term matching'. Together they form a unique fingerprint.

Cite this