FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems

• URL: http://arxiv.org/abs/2210.07768v1
• Date: Mon, 26 Sep 2022 02:31:13 GMT
• Title: FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems
• Authors: Weijie Zhao, Xuewu Jiao, Xinsheng Luo, Jingxue Li, Belhal Karimi, Ping Li
• Abstract summary: We propose a novel end-to-end training framework that pipelines the feature extraction and the training on GPU servers to save the intermediate I/O of the feature extraction. We present a light-weight GPU memory management algorithm that supports dynamic GPU memory allocation with minimal overhead.
• Score: 15.622358361804343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has been widely deployed for online ads systems to predict Click-Through Rate (CTR). Machine learning researchers and practitioners frequently retrain CTR models to test their new extracted features. However, the CTR model training often relies on a large number of raw input data logs. Hence, the feature extraction can take a significant proportion of the training time for an industrial-level CTR model. In this paper, we propose FeatureBox, a novel end-to-end training framework that pipelines the feature extraction and the training on GPU servers to save the intermediate I/O of the feature extraction. We rewrite computation-intensive feature extraction operators as GPU operators and leave the memory-intensive operator on CPUs. We introduce a layer-wise operator scheduling algorithm to schedule these heterogeneous operators. We present a light-weight GPU memory management algorithm that supports dynamic GPU memory allocation with minimal overhead. We experimentally evaluate FeatureBox and compare it with the previous in-production feature extraction framework on two real-world ads applications. The results confirm the effectiveness of our proposed method.

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