Efficient algorithms for massively parallel computers

Author

Hastings, Harold M. ; Kadar, Ivan

Author_Institution

Dept. of Math., Hofstra Univ., Hempstead, NY, USA

fYear

1988

fDate

10-12 Oct 1988

Firstpage

165

Lastpage

167

Abstract

The authors investigate the stability of massively parallel computations using a linear systems approach. Stability is important for several reasons. These include bounding the response of the algorithm to numerical noise so that the typically small amount of local memory can be used efficiently, as well as designing algorithms and even hardware to be fault tolerant. Both Lyapunov stability (insensitivity to small changes in data and to noise) and structural stability (fault tolerance in hardware and software) are studied. The methodology is motivated by neural network modeling but may have larger applications

Keywords

Lyapunov methods; fault tolerant computing; neural nets; parallel algorithms; stability; Lyapunov stability; algorithms; fault tolerant; linear systems approach; local memory; massively parallel computers; neural network modeling; numerical noise; stability; Algorithm design and analysis; Application software; Concurrent computing; Fault tolerance; Hardware; Linear systems; Lyapunov method; Neural networks; Stability; Structural engineering;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers of Massively Parallel Computation, 1988. Proceedings., 2nd Symposium on the Frontiers of

Conference_Location

Fairfax, VA

Print_ISBN

0-8186-5892-4

Type

conf

DOI

10.1109/FMPC.1988.47466

Filename

47466