An efficient parallel ISODATA algorithm based on Kepler GPUs

ISODATA is a well-known clustering algorithm used in various areas. It employs a heuristic strategy allowing the clusters based on the nearest neighbor rule to split and merge as appropriate. However, since the volume of the data to be clustered in real world is growing continuously, the efficiency of serial ISODATA has become a serious practical issue. The GPU (Graphics Processing Unit) is an emerging high performance computing platform due to its highly parallel multithreaded architecture. In this paper, we propose an efficient parallel ISODATA algorithm based on the latest Kepler GPUs and the dynamic parallelism feature in CUDA (Compute Unified Device Architecture). Performance study shows that our parallel ISODATA can achieve promising speedup ratios and features favorable scalability compared to the original algorithm.