2. Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs
Guide: Dr. Rajesh Kanna, Vellore Institute of Technology, India
I presented this work at the 27th International Conference on Supercomputing, ICS 2013, Eugene, Oregon, USA
In this work, we proposed a parallel approach to implement the yConvex Hypergraph model by exploiting massively parallel cores of NVIDIA's Compute Unified Device Architecture (CUDA). We perform our experiments on the MODIS satellite image database by NASA, and based on our analysis ,we observe that the parallel implementation outperforms its sequential counterpart by 2 to 10 times (2x-10x). This work was presented at ICS 2013 in Eugene, Oregon. Extended abstract and the presented poster are here in the Research section.
The basic working of the algorithm can be explained by the figure below.
1. Once the threshold of resolution 2000 X 2000 is reached, the parallel yCHG algorithm proposed by us outperforms the serial counterpart by 2 times to 10 times (2x-10x)
2. Varying the number of hyperedges in an image with constant resolution, does not impact the performance of the overall algorithm. This is due to the fact that our proposed algorithm is directly dependent on the resolution of the input image irrespective of other factors.
 B. Rajesh Kanna, C. Aravindan, and K. Kannan, Development of yConvex hypergraph model for contour-based image analysis, in Proceedings of the 2nd IEEE International Conference Computer Communication and Informatics (ICCCI-2012), 2, 1-5, 2012 http://doi.acm.org/10.1109/ICCCI.2012.6158806
 B. Rajesh Kanna, C. Aravindan, and K. Kannan, Image-based area estimation of any connected region using y-convex region decomposition, AEU -International Journal of Electronics and Communications, 66 (2):172- 183, 2012 DOI: 10.1016/j.aeue.2011.06.010
 B. Rajesh Kanna, C. Aravindan, and K. Kannan, A contour-based scheme for representing arbitrary shapes in digital images, in Proceedings of ACM International Conference and Workshop on Emerging Trends in Computer Applications, 1, 535-540, 2011
 The Digital Database for Screening Mammography, Michael Heath, Kevin Bowyer, Daniel Kopans, Richard Moore and W. Philip Kegelmeyer, in Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., 212-218, Medical Physics Publishing, 2001. ISBN 1-930524-00-5.
 Hall, Dorothy K., George A. Riggs, and Vincent V. Salomonson. 2006, updated daily. MODIS/Terra Snow Cover Daily L3 Global 500m Grid V005, [November 2000 and October 2001]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.