Hierarchical Clustering with CUDA/GPU

Graphics processing units (GPUs) are powerful computational devices tailored towards the needs of the 3-D gaming industry for high-performance, real-time graphics engines. Nvidia Corporation provides a programming language called CUDA for general-purpose GPU programming. Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces; if there are n data points, it can be computed in O(n) to O(n log n) sequential time, depending on the distance metrics employed. The present work explores parallel computation of hierarchical clustering with CUDA/GPU, and obtains an overall speed-up of up to 48 times over sequential computation with an Intel Pentium CPU.