Timezone: »

Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
Weijie Zhao · Deping Xie · Ronglai Jia · Yulei Qian · Ruiquan Ding · Mingming Sun · Ping Li

Mon Mar 04:30 PM -- 07:00 PM PST @ Ballroom A #5
Neural networks of ads systems usually take input from multiple resources, e.g. query-ad relevance, ad features and user portraits. These inputs are encoded into one-hot or multi-hot binary features, with typically only a tiny fraction of nonzero feature values per example. Deep learning models in online advertising industries can have terabyte-scale parameters that do not fit in the GPU memory nor the CPU main memory on a computing node. For example, a sponsored online advertising system can contain more than $10^{11}$ sparse features, making the neural network a massive model with around 10 TB parameters. In this paper, we introduce a distributed GPU hierarchical parameter server for massive scale deep learning ads systems. We propose a hierarchical workflow that utilizes GPU High-Bandwidth Memory, CPU main memory and SSD as 3-layer hierarchical storage. All the neural network training computations are contained in GPUs. Extensive experiments on real-world data confirm the effectiveness and the scalability of the proposed system. A 4-node hierarchical GPU parameter server can train a model more than 2X faster than a 150-node in-memory distributed parameter server in an MPI cluster. In addition, the price-performance ratio of our proposed system is 4-9 times better than an MPI-cluster solution.

Author Information

Weijie Zhao (Baidu Research)
Deping Xie (Baidu)
Ronglai Jia (Baidu)
Yulei Qian (Baidu)
Ruiquan Ding (Baidu)
Mingming Sun (Baidu Research)
Ping Li (Baidu Research)

Related Events (a corresponding poster, oral, or spotlight)