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微光:基于图形处理器的多级多维尺度分析

Glimmer: multilevel MDS on the GPU.

作者信息

Ingram Stephen, Munzner Tamara, Olano Marc

机构信息

Department of Computer Science, University of British Columbia, Vancouver, Canada.

出版信息

IEEE Trans Vis Comput Graph. 2009 Mar-Apr;15(2):249-61. doi: 10.1109/TVCG.2008.85.

Abstract

We present Glimmer, a new multilevel algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hardware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm makes local minima less likely while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We also show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm.

摘要

我们展示了Glimmer,一种用于多维缩放的新型多级算法,旨在利用现代图形处理单元(GPU)硬件。我们还展示了GPU-SF,它是Glimmer使用的基于力的并行子系统。Glimmer将输入组织成一个层次结构,并递归地应用GPU-SF来合并和细化这些层次。该算法的多级特性降低了陷入局部最小值的可能性,而GPU并行性提高了计算速度。我们基于归一化应力函数的滤波近似为GPU-SF提出了一个鲁棒的终止条件。针对一系列合成和真实基准数据集,我们展示了Glimmer在速度、归一化应力和视觉质量方面相对于几种先前算法的优势。我们还表明,Glimmer在GPU上的性能比同一算法的CPU实现要快得多。

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