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Rapid Performance of a Generalized Distance Calculation

Scott Fisackerly, Eric Chu, David L. Foster
Electrical and Computer Engineering Department, Kettering University, Flint, MI, USA
The 2011 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’11), 2011

@article{fisackerly2011rapid,

   title={Rapid Performance of a Generalized Distance Calculation},

   author={Fisackerly, S. and Chu, E. and Foster, D.L.},

   year={2011}

}

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The ever-increasing size of data sets and the need for real-time processing drives the need for high speed analysis. Since traditional CPUs are designed to execute a small number of sequential process, they are ill-suited to keep pace with this growth and exploit the massive parallelism inherent in these problem spaces. In the last several years, the parallelism of GPUs has made them a viable solution for general purpose computing. However, effective use of GPUs requires a significantly different programming paradigm. Towards the goal of creating a function library that maximizes the performance improvement of GPUs in data analysis and clustering, this paper presents an implementation of a general n-dimensional distance calculation commonly used in these types of algorithms. Experimental results show up to a 390x speedup using a Tesla C1060 and up to a 538x speedup using a GeForce GTX 480 over an Intel Core i7.
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