Computing large-scale distance matrices on GPU

A distance matrix is simply an n×n 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.