Flocking-based Document Clustering on the Graphics Processing Unit

Analyzing and grouping documents by content is a complex problem. One explored method of solving this problem borrows from nature, imitating the fl ocking behavior of birds. Each bird represents a single document and fl ies toward other documents that are similar to it. One limitation of this method of document clustering is its complexity O(n2). As the number of documents grows, it becomes increasingly diffi cult to receive results in a reasonable amount of time. However, fl ocking behavior, along with most naturally inspired algorithms such as ant colony optimization and particle swarm optimization, are highly parallel and have experienced improved performance on expensive cluster computers. In the last few years, the graphics processing unit (GPU) has received attention for its ability to solve highly-parallel and semi-parallel problems much faster than the traditional sequential processor. Some applications see a huge increase in performance on this new platform. The cost of these high-performance devices is also marginal when compared with the price of cluster machines. In this paper, we have conducted research to exploit this architecture and apply its strengths to the document flocking problem. Our results highlight the potential benefi t the GPU brings to all naturally inspired algorithms. Using the CUDA platform from NVIDIA®, we developed a document fl ocking implementation to be run on the NVIDIA® GEFORCE 8800. Additionally, we developed a similar but sequential implementation of the same algorithm to be run on a desktop CPU. We tested the performance of each on groups of news articles ranging in size from 200 to 3,000 documents. The results of these tests were very signifi cant. Performance gains ranged from three to nearly fi ve times improvement of the GPU over the CPU implementation. This dramatic improvement in runtime makes the GPU a potentially revolutionary platform for document clustering algorithms