Performance assessment of parallel spectral analysis: Towards a practical performance model for parallel medical applications

F. Munz, T. Ludwig, S. Ziegler, P. Bartenstein, M. Schwaiger, A. Bode

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We present a parallel, medical application for the analysis of dynamic positron emission tomography (PET) images together with a practical performance model. The parallel application improves the diagnosis for a patient (e. g. in epilepsy surgery) because it enables the fast computation of parametric images on a pixel level in contrast to the traditionally used region of interest (ROI) approach. We derive a simple performance model from the application context and demonstrate the accuracy of the model to predict the runtime of the application on a NOW. The model is used to determine an optimal value for the length of the messages with regard to the per message overhead and the load imbalance.

Original languageEnglish
Title of host publicationHigh-Performance Computing and Networking - 7th International Conference, HPCN Europe 1999, Proceedings
EditorsPeter Sloot, Alfons Hoekstra, Bob Hertzberger, Marian Bubak
PublisherSpringer Verlag
Pages430-439
Number of pages10
ISBN (Print)3540658211, 9783540658214
DOIs
StatePublished - 1999
Event7th International Conference on High-Performance Computing and Networking, HPCN Europe 1999 - Amsterdam, Netherlands
Duration: 12 Apr 199914 Apr 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1593
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on High-Performance Computing and Networking, HPCN Europe 1999
Country/TerritoryNetherlands
CityAmsterdam
Period12/04/9914/04/99

Keywords

  • Medical application
  • Network of workstations
  • PVM
  • Parallel kinetic modeling
  • Positron emission tomography
  • Practical performance prediction

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