Bayesian Sequential Partitioning for Big Data Analysis on Heterogeneous Manycore Systems

• Main Authors: Li, Kun-Chun, 李坤駿
• Other Authors: Lai, Bo-Cheng
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/wvexk9

博士 === 國立交通大學 === 電子工程學系 電子研究所 === 102 === Uncovering information from the large volume of data in a timely manner has been an imperative task in the next wave of computing technologies. Estimating the density distribution of the high dimensional data samples is an effective method to comprehend the characteristics of the data space. Bayesian Sequential Partitioning (BSP) is a statistically effective density estimation algorithm for high dimensional data. However, BSP is computationally expensive due to complex statistical model, and data intensive when counting the large volume samples. This thesis proposes a high performance design of BSP by leveraging the powerful computation capability of a heterogeneous many-core system. A series of techniques are implemented on both algorithm flow and data management policies. With the proposed approaches, the performance bottleneck is alleviated and the runtime is significantly improved. The overall speedup of the BSP analysis on a heterogeneous many-core system can reach up to 155.3x, when compared with the reference design on a high-end CPU.