Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F11%3A00189320" target="_blank" >RIV/68407700:21340/11:00189320 - isvavai.cz</a>
Result on the web
<a href="http://gcoe-mi.jp/english/publish_list/pub_inner/id:4/cid:17" target="_blank" >http://gcoe-mi.jp/english/publish_list/pub_inner/id:4/cid:17</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods
Original language description
Modern GPUs are well suited for performing image processing tasks. We utilize their high computational performance and memory bandwidth for image segmentation purposes. We segment cardiac MRI data by means of numerical solution of an anisotropic partialdifferential equation of the Allen-Cahn type. We implement two different algorithms for solving the equation on the CUDA architecture. One of them is based on the Runge-Kutta-Merson method for the approximation of solutions of ordinary differential equations, the other uses the GMRES method for the numerical solution of systems of linear equations. In our experiments, the CUDA implementations of both algorithms are about 3--9 times faster than corresponding 12-threaded OpenMP implementations.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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