Exploiting data parallelism in the yConvex hypergraph algorithm for image representation using GPGPUs


To define and identify a region-of-interest (ROI) in a digital image, the shape descriptor of the ROI has to be described in terms of its boundary characteristics. In this work, we propose a parallel approach to implement the yCHG (yConvex Hypergraph Model) 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 performance of the serial implementation is better on smaller images, but once the threshold is achieved in terms of image resolution, the parallel implementation outperforms its sequential counterpart by 2 to 10 times (2x-10x). We also conclude that an increase in the number of hyperedges in the ROI of a given size does not impact the performance of the overall algorithm.

ICS 2013