Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/49020
Title: Distribution of image processing applications on a heterogeneous workstation network: modeling, load balancing, and experimental results
Authors: Hernández-Sosa, Daniel 
Cabrera-Gámez, Jorge 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Knowledge based vision systems (Kbvs)
Parallel virtual machine (Pvm)
Distributed image processing
Load-balancing
Issue Date: 1997
Journal: Proceedings of SPIE - The International Society for Optical Engineering 
Conference: Parallel and Distributed Methods for Image Processing 
Abstract: This work analyzes the computation distribution in applications generated by a multilevel knowledge-based system for image processing called SVEX. This distribution has been carried out on a heterogeneous workstation network, trying to take advantage of the availability and frequent infra- utilization of this computational resource. The parallelization is based on message-passing tool parallel virtual machine (PVM). Firstly SVEX and its computational scheme are described, detailing the structure of the first level (the pixel processor). Then different distribution paradigms are studied, selecting for its implementation the parallelism based on the data. Considering this alterative, the research addresses two fundamental problems: analysis of basic load-balancing schemes and obtaining a model for predicting parallelization behavior as new machines are added to the computational network. The results produced in a series of experiments permit the comparison of load-balancing schemes and the validation of the proposed model. The experiments include the processing of both static images and sequences.
URI: http://hdl.handle.net/10553/49020
ISSN: 0277-786X
DOI: 10.1117/12.279615
Source: Proceedings of SPIE - The International Society for Optical Engineering [ISSN 0277-786X], v. 3166, p. 176-185
Appears in Collections:Actas de congresos
Show full item record

Page view(s)

38
checked on Feb 27, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.