Parallel implementation of a density-based stream clustering algorithm over a GPU scheduling system

Graphics Processing Units (GPUs) are used together with the CPU to accelerate a wide range of general purpose applications or scientific computations. The highly parallel architecture of the GPU consists of hundreds of cores optimized for parallel performance. Applications taking benefit of the GPU architecture have to be implemented according to the GPU parallel concept. An algorithm which follows a sequential work flow, has to be redesigned to achieve good performance on the GPU device. DenStream is a recent stream clustering algorithm that consists of two main parts. The online part summarizes data from the data stream, and builds micro clusters, while the offline part generates the final clustering using density-based clustering. In this work, we present a GPU-based efficient implementation of DenStream called (G-DenStream). G-DenStream is faster than DenStream, especially when the dimensionality of the streaming dataset increases, while keeping the quality of the reflected clustering as it is. The implementations in this work achieve palatalization of both online and offline parts and test the performance and the utilization on the GPU.