Accelerating binary biclustering on platforms with CUDA-enabled GPUs

摘要

Data mining is nowadays essential in many scientific fields to extract valuable information from large input datasets and transform it into an understandable structure. For instance, biclustering techniques are very useful in identifying subsets of two-dimensional data where both rows and columns are correlated. However, some biclustering techniques have become extremely time-consuming when processing very large datasets, which nowadays prevents their use in many areas of research and industry (such as bioinformatics) that have experienced an explosive growth on the amount of available data. In this work we present CUBiBit, a tool that accelerates the search for relevant biclusters on binary data by exploiting the computational capabilities of CUDA-enabled GPUs as well as the several CPU cores available in most current systems. The experimental evaluation has shown that CUBiBit is up to 116 times faster than the fastest state-of-the-art tool, BiBit, in a system with two Intel Sandy Bridge processors (16 CPU cores) and three NVIDIA K20 GPUs. CUBiBit is publicly available to download from https://sourceforge.netiprojects/cubibit. (C) 2018 Elsevier Inc. All rights reserved.