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	TencentBoost: A Gradient Boosting Tree System with Parameter Server
	Jiang, Jie ; Jiang, Jiawei ; Cui, Bin ; Zhang, Ce
	2017
英文摘要	Gradient boosting tree (GBT), a widely used machine learning algorithm, achieves state-of-the-art performance in academia, industry, and data analytics competitions. Although existing scalable systems which implement GBT, such as XGBoost and MLlib, perform well for datasets with medium-dimensional features, they can suffer performance degradation for many industrial applications where the trained datasets contain high-dimensional features. The performance degradation derives from their inefficient mechanisms for model aggregation- either map-reduce or all-reduce. To address this high-dimensional problem, we propose a scalable execution plan using the parameter server architecture to facilitate the model aggregation. Further, we introduce a sparse-pull method and an efficient index structure to increase the processing speed. We implement a GBT system, namely TencentBoost, in the production cluster of Tencent Inc. The empirical results show that our system is 2-20x faster than existing platforms.; National Natural Science Foundation of China [61572039]; 973 program [2014CB340405]; Shenzhen Gov Research Project [JCYJ20151014093505032]; Tecent Research Grant (PKU); CPCI-S(ISTP); 281-284
语种	英语
出处	IEEE 33rd International Conference on Data Engineering (ICDE)
DOI标识	10.1109/ICDE.2017.87
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/469897]
专题	信息科学技术学院 软件与微电子学院
推荐引用方式 GB/T 7714	Jiang, Jie,Jiang, Jiawei,Cui, Bin,et al. TencentBoost: A Gradient Boosting Tree System with Parameter Server. 2017-01-01.

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