VDBSCAN+:基于GPU并行的性能优化

2013 International Conference on Parallel and Distributed Computing, Applications and Technologies Pub Date : 2013-12-16 DOI:10.1109/PDCAT.2013.11

C. R. Valêncio, Guilherme Prióli Daniel, C. D. Medeiros, A. Cansian, L. Baida, Fernando Ferrari

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引用次数: 1

摘要

空间数据挖掘技术能够从空间数据库中提取知识。然而，高计算成本和算法的复杂性是该领域的主要问题。本研究提出了一种名为VDBSCAN+的新算法，该算法源自VDBSCAN(基于噪声的应用程序的变密度空间聚类)算法，并专注于在GPU(图形处理单元)中使用并行技术，与VDBSCAN相比，通过将运行时间增加95%，获得了显著的性能改进。

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VDBSCAN+: Performance Optimization Based on GPU Parallelism

Spatial data mining techniques enable the knowledge extraction from spatial databases. However, the high computational cost and the complexity of algorithms are some of the main problems in this area. This work proposes a new algorithm referred to as VDBSCAN+, which derived from the algorithm VDBSCAN (Varied Density Based Spatial Clustering of Applications with Noise) and focuses on the use of parallelism techniques in GPU (Graphics Processing Unit), obtaining a significant performance improvement, by increasing the runtime by 95% in comparison with VDBSCAN.

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2013 International Conference on Parallel and Distributed Computing, Applications and Technologies

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