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/.
Highlights
The analysis of biological networks is an important task for gaining insights into the massive amount of data generated by high-throughput technologies
The computational tests are performed on following hardware setup: a total of four NVIDIA Tesla C2050 GPU cards are installed on a X8DTG-Q Supermicro server that has 2| Intel Xeon E5620 2.4GHz processors, 32GB of 1066 MHz DDR3 RAM and 800GB of Local Hard Disk
The published clinical data gives the clinical metastasis; we consider this as a phenotypical dummy probe and keep it as a row in the input matrix
Summary
The analysis of biological networks is an important task for gaining insights into the massive amount of data generated by high-throughput technologies (e.g., microarrays). For a set R of m reference points and a set Q of n query points in a d-dimensional space, the kNN search problem identifies the k-nearest neighbours of each query point q[Q in the reference set R, given a distance metric [1]. 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
