Efficient Hierarchical Agglomerative Clustering Algorithms on GPU Using Data Partitioning

S. Shalom, M. Dash
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引用次数: 6

Abstract

We explore the capabilities of today's high-end Graphics processing units (GPU) on desktops to efficiently perform hierarchical agglomerative clustering (HAC) through partitioning of data. Traditional HAC has high time and memory complexities leading to low clustering efficiencies. We reduce time and memory bottlenecks of the traditional HAC algorithm by exploring the performance capabilities of the GPU, significantly accelerating the computations without compromising the accuracy of clusters. We implement the traditional HAC and the Partially Overlapping Partitioning (PoP) on GPU using Compute Unified Device Architecture (CUDA) and compare the computational performance with CPU using micro array data. The result shows that the PoP HAC and traditional HAC are up to 442 times and 66 times faster on the GPU respectively than the time taken by CPU. The PoP-enabled HAC on GPU requires only a fraction of the memory required by traditional HAC both on the CPU and GPU.
基于数据分区的GPU高效分层聚类算法
我们探讨了当今桌面上高端图形处理单元(GPU)通过数据分区高效执行分层聚合集群(HAC)的能力。传统的HAC具有较高的时间和内存复杂性,导致集群效率较低。我们通过探索GPU的性能能力来减少传统HAC算法的时间和内存瓶颈,在不影响集群准确性的情况下显著加速计算。我们使用CUDA在GPU上实现了传统的HAC和部分重叠分区(PoP),并使用微阵列数据与CPU的计算性能进行了比较。结果表明,PoP HAC和传统HAC在GPU上的运行速度分别比CPU快442倍和66倍。GPU上启用pop的HAC只需要CPU和GPU上传统HAC所需内存的一小部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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