Issue title: Selected papers from the ISCA International Conference on Software Engineering and Data Engineering,
and the ISCA International Conference on Computer Applications in Industry and Engineering, 2015, and Invited Papers
Guest editors: Takaaki Goto and Narayan C. Debnath
Affiliations: Department of Mathematics and Computer Science, South Dakota School of Mines and Technology, Rapid City, SD 57701-3995, USA
Correspondence:
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Corresponding author: John M. Weiss, Department of Mathematics and Computer Science, South Dakota School of Mines and Technology, Rapid City, SD 57701-3995, USA. E-mail:john.weiss@sdsmt.edu
Abstract: Thinning is an important task in many image processing applications,
including remote sensing, photogrammetry, optical character recognition, and
medical imaging. In this study, we compare the performance of thinning
algorithms on parallel hardware. Grayscale thinning involves a substantial
amount of computation per pixel, and may be accelerated in several ways:
algorithmic improvements, code optimization, and parallelization. We
describe an algorithmic improvement that speeds up grayscale thinning
several-fold, and demonstrate scalable acceleration from multi-core CPU
concurrency libraries (such as OpenMP), coprocessor hardware (such as the
Xeon Phi), and GPUs (such as CUDA-enabled NVIDIA graphics cards). GPU
processing appears to offer the most cost-effective approach for high
performance grayscale thinning applications.
