Parallelizing Hilbert-Huang Transform on a GPU

Pulung Waskito, Shinobu Miwa, Y. Mitsukura, H. Nakajo
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引用次数: 13

Abstract

In this paper, we show parallel implementation of Hilbert-Huang Transform on GPU. This implementation focused on the reducing the computation complexity from O(N) on a single CPU to O(N/P log (N)) on GPU, as well as the use of 'shared-global' switching method to increase performance. Evaluation results show our single GPU implementation using Tesla C1060 achieves 29.0x speedup in best case, and a total of 7.1x speedup for all results when compared to a single Intel dual core CPU.
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在GPU上并行化Hilbert-Huang变换
本文给出了Hilbert-Huang变换在GPU上的并行实现。该实现侧重于将计算复杂度从单个CPU上的O(N)降低到GPU上的O(N/P log (N)),以及使用“全局共享”切换方法来提高性能。评估结果显示,我们使用Tesla C1060的单个GPU实现在最佳情况下实现了29.0倍的加速,与单个Intel双核CPU相比,所有结果的加速总和为7.1倍。
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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