NDLI: Design and Implementation of Cover Tree Algorithm on CUDA-Compatible GPU

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Design and Implementation of Cover Tree Algorithm on CUDA-Compatible GPU

Content Provider	Semantic Scholar
Author	Sharma, Mukesh Joshi, Ramesh Chandra
Copyright Year	2010
Abstract	Recently developed architecture such as Compute Unified Device Architecture (CUDA) allows us to exploit the computational power of Graphics Processing Units (GPU). In this paper we propose an algorithm for implementation of Cover tree, accelerated on the graphics processing unit (GPU). The existing algorithm for Cover Tree implementation is for single core CPU and is not suitable for applications with large data set such as phylogenetic analysis in bioinformatics, in order to find nearest neighbours in real time. As far as we know this is first attempt made ever to implement the cover tree on GPU. The proposed algorithm has been implemented using compute unified device architecture (CUDA), which is available on the NVIDIA GPU. The proposed algorithm efficiently uses on chip shared memory in order to reduce the data amount being transferred between offchip memory and processing elements in the GPU. Furthermore our algorithm presents a model to implement other distance trees on the GPU. We show some experimental results comparing the proposed algorithm with it's execution on pre-existing single core architecture. The results show that the proposed algorithm has a significant speedup as compare to the single core execution of this code.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ijcaonline.org/volume3/number8/pxc3871057.pdf
Alternate Webpage(s)	http://www.ijcaonline.org/archives/volume3/number8/748-1057?format=pdf
Alternate Webpage(s)	https://www.ijcaonline.org/archives/volume3/number8/748-1057?format=pdf
Language	English
Access Restriction	Open
Subject Keyword	Algorithm Bioinformatics CPU (central processing unit of computer system) CUDA Central processing unit Computer graphics Cover tree Graphics processing unit Intel Core (microarchitecture) Phylogenetics Shared memory Speedup Trees (plant)
Content Type	Text
Resource Type	Article

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