Jarvis, Stephen A., 1970-, Foley, B. P., Isitt, P. J., Spooner, Daniel P., Rueckert, D. and Nudd, G. R. (2008) Performance prediction for a code with data-dependent runtimes. In: 4th UK e-Science All Hands Meeting (AHM 2005), Nottingham, England, Sep 19-22, 2005. Published in: Concurrency and Computation: Practice & Experience, Vol.20 (No.3 Sp. Iss. SI). pp. 195-206.

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Abstract

In this paper we present a predictive performance model for a key biomedical imaging application found as part of the U.K. e-Science Information eXtraction from Images (IXI) project. This code represents a significant challenge for our existing performance prediction tools as it has internal structures that exhibit highly variable runtimes depending on qualities in the input data provided. Since the runtime can vary by more than an order of magnitude, it has been difficult to apply meaningful quality of service criteria to workflows that use this code. The model developed here is used in the context of an interactive scheduling system which provides rapid feedback to the users, allowing them to tailor their workloads to available resources or to allocate extra resources to scheduled workloads. Copyright (c) 2007 John Wiley & Sons, Ltd.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Computer Science
Journal or Publication Title:	Concurrency and Computation: Practice & Experience
Publisher:	John Wiley & Sons Ltd.
ISSN:	1532-0626
Official Date:	10 March 2008
Volume:	Vol.20
Number:	No.3 Sp. Iss. SI
Number of Pages:	12
Page Range:	pp. 195-206
Identification Number:	10.1002/cpe.1191
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Title of Event:	4th UK e-Science All Hands Meeting (AHM 2005)
Type of Event:	Conference
Location of Event:	Nottingham, England
Date(s) of Event:	Sep 19-22, 2005
URI:	http://wrap.warwick.ac.uk/id/eprint/30430

Data sourced from Thomson Reuters' Web of Knowledge

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