Performance Analysis of kNN on large datasets using CUDA & Pthreads
Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems
Blekinge Institute of Technology, 2015
@article{kankatala2015performance,
title={Performance Analysis of kNN on large datasets using CUDA & Pthreads},
author={Kankatala, Sriram},
journal={Electrical Engineering},
year={2015}
}
Several organizations have large databases which are growing at a rapid rate day by day, which need to be regularly maintained. Content based searches are similar searched based on certain features that are obtained from various multi media data. For various applications like multimedia content retrieval, data mining, pattern recognition, etc., performing the nearest neighbor search is a challenging task in multidimensional data. The important factors in nearest neighbor search kNN are searching speed and accuracy. Implementation of kNN on GPU is an ongoing research from last few years, focusing on improving the performance of kNN. By considering these aspects, our research has been started and found a gap in this research area. This master thesis shows effective and efficient parallelism on multi-core of CPU and GPU to compare the performance with single core CPU. This paper shows an experimental implementation of kNN on single core CPU, Mutli-core CPU and GPU using C, Pthreads and CUDA respectively. We considered different levels of inputs (size, dimensions) to evaluate the performance. The experiment shows the GPU outperforms for kNN when compared to CPU single core with a factor of approximately 5.8 to 16 and CPU multi-core with a factor of approximately 1.2 to 3 for different levels of inputs.
March 5, 2016 by hgpu
