arXiv:1506.02226cs.DC
Design and optimization of DBSCAN Algorithm based on CUDA
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Abstract
DBSCAN is a very classic algorithm for data clus- tering, which is widely used in many fields. However, with the data scale growing much more bigger than before, the traditional serial algorithm can not meet the performance requirement. Recently, parallel computing based on CUDA has developed very fast and has great advantage on big data. This paper summarizes the algorithms proposed before and improves the performance of the old DBSCAN algorithm by using CUDA and parallel computing. The algorithm uses shared memory as much as possible compared with other algorithms and it has very good scalability. A data set is tested on the new version of DBSCAN. Finally, we analyze the results and give a conclusion that our algorithm is approximately 97 times faster than the serial version.
A Google search reveals this paper: https://arxiv.org/abs/1506.02226 This paper claims a "97x improvement" over traditional (non-parallelized) DBSCAN algorithms, but that's not a very helpful claim, because it does not indicate what the computational costs are as a function of…
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