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Preprint (Vorabdruck) zugänglich unter
High performance cluster computing with 3-D nonlinear diffusion filters
URN: urn:nbn:de:bsz:291-scidok-44330
URL: http://scidok.sulb.uni-saarland.de/volltexte/2012/4433/
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Dokument 1.pdf (371 KB)
Freie Schlagwörter (Englisch):
additive operator splitting , cluster computing
Institut:
DDC-Sachgruppe:
Mathematik
Dokumentart:
Preprint (Vorabdruck)
Schriftenreihe:
Preprint / Fachrichtung Mathematik, Universität des Saarlandes
Bandnummer:
87
Sprache:
Englisch
Erstellungsjahr:
2003
Publikationsdatum:
04.01.2012
Kurzfassung auf Englisch:
This paper deals with parallelisation and implementation aspects of PDE-based image processing models for large cluster environments with distributed memory. As an example we focus on nonlinear diffusion filtering which we discretise by means of an additive operator splitting (AOS). We start by decomposing the algorithm into small modules that shall be parallelised separately. For this purpose image partitioning strategies are discussed and their impact on the communication pattern and volume is analysed. Based on the results we develop an algorithmic implementation with excellent scaling properties on massively connected low latency networks. Test runs on a high-end Myrinet cluster yield almost linear speedup factors up to 209 for 256 processors. This results in typical denoising times of 0.5 seconds for five iterations on a 256 x 256 x 128 data cube.
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