Files in this item



application/pdf9026360.pdf (4MB)Restricted to U of Illinois
(no description provided)PDF


Title:Partitioning algorithms for parallel circuit simulation
Author(s):Yeh, David Ching-kai
Doctoral Committee Chair(s):Rao, Vasant B.
Department / Program:Engineering, Electronics and Electrical
Discipline:Engineering, Electronics and Electrical
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Engineering, Electronics and Electrical
Abstract:Circuit simulation is an indispensable tool in the design and analysis of Very Large Scale Integrated (VLSI) circuits. The most widely used circuit simulators rely on direct methods and offer the most accurate, reliable, and technology-independent means of simulating integrated circuits. The simulation process is inherently very computation intensive and, hence, can require a significant portion of the computational resources available for the development of VLSI circuits. With the use of multiprocessor computers becoming more widespread, there exists an opportunity to speed up the simulation by partitioning the circuit so that the computation may be spread among the processors. To accomplish this, the circuit is partitioned into subcircuits using a node tearing method. If the circuit matrix is ordered subcircuit by subcircuit followed by the tearing nodes, then the matrix takes a bordered-block-diagonal form and the LU-factorization of the diagonal blocks may take place in parallel. This thesis defines the important objectives for this partitioning task and presents two algorithms that may be used to meet the partitioning goals. The first algorithm is an iterative improvement algorithm and the second is a network flow algorithm. Partitioning results and speedups are given for a variety of circuits.
Issue Date:1990
Rights Information:Copyright 1990 Yeh, David Ching-kai
Date Available in IDEALS:2011-05-07
Identifier in Online Catalog:AAI9026360
OCLC Identifier:(UMI)AAI9026360

This item appears in the following Collection(s)

Item Statistics