  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.

Distributed, Parallel, and Cluster Computing;Machine Learning;Machine Learning