Fault-tolerant recursive least-squares computations on a mesh-connected parallel processor

Authors:
Albert Y. Zomaya;Adrian Yates;Stephan Olariu
Affiliations:
School of Information Technologies, The University of Sydney, Sydney, NSW, Australia;Parallel Computing Research Laboratory, Department of Electrical and Electronic Engineering, The University of Western Australia, Australia;Department of Computer Science, Old Dominion University, Norfolk, Virginia
Venue:
Journal of Parallel and Distributed Computing
Year:
2002

Citing 9
Cited 0

Fault Tolerance Techniques for Array Structures Used in Supercomputing

Computer
System identification: theory for the user

System identification: theory for the user
Numerical recipes in C: the art of scientific computing

Numerical recipes in C: the art of scientific computing
A Taxonomy of Reconfiguration Techniques for Fault-Tolerant Processor Arrays

Computer
Effective reconfiguration algorithms in fault tolerant mesh-connected networks

Australian Computer Journal
An evaluation of reconfiguration algorithms in fault tolerant processor arrays

Proceedings of the fifth MIT conference on Advanced research in VLSI
Modelling and Simulation of Robot Manipulators: A Parallel Processing Approach

Modelling and Simulation of Robot Manipulators: A Parallel Processing Approach
Partitioning and Scheduling Parallel Programs for Multiprocessors

Partitioning and Scheduling Parallel Programs for Multiprocessors
Fault-Tolerant Meshes and Hypercubes with Minimal Numbers of Spares

IEEE Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recursive least squares (RLS) is a popular iterative method used for the modeling of systems while in operation. RLS provides an estimate for unknown parameters of a system based on some known parameters and inputs and outputs of that system. This technique is used frequently in digital signal processing and control applications, where it is not possible to completely determine the current state of the system. The RLS procedure incurs intensive computations in every iteration of the algorithm. To implement RLS in situ at a reasonable sampling rate, the complexity of the system's model must be reduced, or the available computing power must be increased. This paper examines methods for increasing the computing power by implementing RLS algorithms on a parallel processing platform. While there has been a large body of research on using parallel processors for the computation of adaptive algorithms, little of this research has examined fault tolerant aspects. As fault tolerance is a critical aspect of any real-time system, this work will examine some factors that should be considered when implementing a real-time adaptive algorithm on a parallel processor system.