28 September 2016 The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology
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Proceedings Volume 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016; 1003155 (2016) https://doi.org/10.1117/12.2249352
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 2016, Wilga, Poland
Abstract
The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.
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Leonid Timchenko, Leonid Timchenko, Andrii Yarovyi, Andrii Yarovyi, Nataliya Kokriatskaya, Nataliya Kokriatskaya, Svitlana Nakonechna, Svitlana Nakonechna, Ludmila Abramenko, Ludmila Abramenko, Tomasz Ławicki, Tomasz Ławicki, Piotr Popiel, Piotr Popiel, Laura Yesmakhanova, Laura Yesmakhanova, } "The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology", Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 1003155 (28 September 2016); doi: 10.1117/12.2249352; https://doi.org/10.1117/12.2249352
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