Please use this identifier to cite or link to this item: doi:10.22028/D291-26241
Title: High performance cluster computing with 3-D nonlinear diffusion filters
Author(s): Bruhn, Andrés
Jakob, Tobias
Fischer, Markus
Kohlberger, Timo
Weickert, Joachim
Brüning, Ulrich
Schnörr, Christoph
Language: English
Year of Publication: 2003
Free key words: additive operator splitting
cluster computing
DDC notations: 510 Mathematics
Publikation type: Other
Abstract: 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.
Link to this record: urn:nbn:de:bsz:291-scidok-44330
Series name: Preprint / Fachrichtung Mathematik, Universität des Saarlandes
Series volume: 87
Date of registration: 4-Jan-2012
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Mathematik
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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