Scalable clustering using multiple GPUs

K-Means is a popular clustering algorithm with wide applications in Computer Vision, Data mining, Data Visualization, etc. Clustering is an important step for indexing and searching of documents, images, video, etc. Clustering large numbers of high-dimensional vectors is very computation intensive. In this paper, we present the design and implementation of the K-Means clustering algorithm on the modern GPU. All steps are performed entirely on the GPU efficiently in our approach. We also present a load balanced multi-node, multi-GPU implementation which can handle up to 6 million, 128-dimensional vectors. We use efficient memory layout for all steps to get high performance. The GPU accelerators are now present on high-end workstations and low-end laptops. Scalability in the number and dimensionality of the vectors, the number of clusters, as well as in the number of cores available for processing are important for usability to different users. Our implementation scales linearly or near-linearly with different problem parameters. We achieve up to 2 times increase in speed compared to the best GPU implementation for K-Means on a single GPU. We obtain a speed up of over 170 on a single Nvidia Fermi GPU compared to a standard sequential implementation. We are able to execute one iteration of K-Means in 136 seconds on off-the-shelf GPUs to cluster 6 million vectors of 128 dimensions into 4K clusters and in 2.5 seconds to cluster 125K vectors of 128 dimensions into 2K clusters.