Towards high performance data analytic on heterogeneous many-core systems: A study on Bayesian Sequential Partitioning

Bo-Cheng Lai*, Tung Yu Wu, Tsou Han Chiu, Kun Chun Li, Chia Ying Lee, Wei Chen Chien, Wing Hung Wong

*此作品的通信作者

研究成果: Article同行評審

摘要

Bayesian Sequential Partitioning (BSP) is a statistically effective density estimation method to comprehend the characteristics of a high dimensional data space. The intensive computation of the statistical model and the counting of enormous data have caused serious design challenges for BSP to handle the growing volume of the data. This paper proposes a high performance design of BSP by leveraging a heterogeneous CPU/GPGPU system that consists of a host CPU and a K80 GPGPU. A series of techniques, on both data structures and execution management policies, is implemented to extensively exploit the computation capability of the heterogeneous many-core system and alleviate system bottlenecks. When compared with a parallel design on a high-end CPU, the proposed techniques achieve 48x average runtime enhancement while the maximum speedup can reach 78.76x.

原文English
頁(從 - 到)36-50
頁數15
期刊Journal of Parallel and Distributed Computing
122
DOIs
出版狀態Published - 1 12月 2018

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