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Cost-effective HPC clustering for computer vision applications

Dietlmeier, Julia orcid logoORCID: 0000-0001-9980-0910, Begley, Seán and Whelan, Paul F. orcid logoORCID: 0000-0001-9230-7656 (2008) Cost-effective HPC clustering for computer vision applications. In: IMVIP 2008 - International Machine Vision and Image Processing Conference, 3-5 September 2008, Portrush, Northern Ireland. ISBN 978-0-7695-3332-2

Abstract
We will present a cost-effective and flexible realization of high performance computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU parallel Knoppix package with message passing interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:computer vision; message passing; parallel programming; workstation clusters;
Subjects:UNSPECIFIED
DCU Faculties and Centres:Research Institutes and Centres > Research Institute for Networks and Communications Engineering (RINCE)
Published in: Proceedings of the 2008 International Machine Vision and Image Processing Conference. . Institute of Electrical and Electronics Engineers. ISBN 978-0-7695-3332-2
Publisher:Institute of Electrical and Electronics Engineers
Official URL:http://dx.doi.org/10.1109/IMVIP.2008.8
Copyright Information:©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Funders:Higher Education Authority
ID Code:15574
Deposited On:27 Jul 2010 13:31 by DORAS Administrator . Last Modified 13 Dec 2019 16:27
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