A distance matrix is simply an nxn two-dimensional array that contains pairwise distances of a set of n points in a metric space. It has a wide range of usage in several fields of scientific research e.g., data clustering, machine learning, pattern recognition, image analysis, information retrieval, signal processing, bioinformatics etc. However, as the size of n increases, the computation of distance matrix becomes very slow or incomputable on traditional general purpose computers. In this paper, we propose an inexpensive and scalable data-parallel solution to this problem by dividing the computational tasks and data on GPUs. We demonstrate the performance of our method on a set of real-world biological networks constructed from a renowned breast cancer study.
7th International Conference on Computer Science & Education (ICCSE 2012). Proceedings of 2012 7th International Conference on Computer Science & Education (Melbourne 14-17 July, 2012) p. 576-580