GPU-FS-kNN: A Software Tool for Fast and Scalable kNN Computation Using GPUs

Abstract

BackgroundThe analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers). An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU), can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem.ResultsWe propose an efficient parallel formulation of the k-Nearest Neighbour (kNN) search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour) for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50–60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets.ConclusionOur GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN) provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL) at https://sourceforge.net/p/gpufsknn/.

Solutions for Processing K Nearest Neighbor Joins for Massive Data on MapReduce

“…Experiments in [21] suggest using GPUs to significantly improve the performance of distance computation, but this is still not applicable for large datasets that cannot reasonably be processed on a single machine.…”

Knowledge discovery by accuracy maximization

“…formulation of the kNN search problem based on graphics processing units are proposed (26), observing speed-ups of 50-60 times compared with central processing unit implementation. Some tests of KODAMA performed with kNN, SVM, or PCA-CA-kNN are provided in SI Appendix both for synthetic (Tables S3 and S4) and experimental (Table S5) datasets.…”

Fast Approximate k-Nearest Neighbours Search Using GPGPU

“…Barrientos et al [18] improved nearest neighbour computation on GPU by using parallel lists of clusters (LC) and SS-index strategies in order to perform fast range search and k-NN, respectively. Arefin et al [19] have recently proposed fast and scalable k-NN computation using GPUs, where the distance matrix is divided into smaller chunks in order to parallelise distance calculations and k-NN search over these sub-matrices. Sismanis et al [20] have recently proposed a parallel k-NN implementation by using truncated bitonic sort in order to speed up the query computation.…”