G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale
  Recommender Systems
Youshao Xiao Shangchun Zhao Zhenglei Zhou Zhaoxin Huan Lin Ju Xiaolu Zhang Lin Wang Jun Zhou
Recently, a new paradigm, meta learning, has been widely applied to Deep
Learning Recommendation Models (DLRM) and significantly improves statistical
performance, especially in cold-start scenarios. However, the existing systems
are not tailored for meta learning based DLRM models and have critical problems
regarding efficiency in distributed training in the GPU cluster. It is because
the conventional deep learning pipeline is not optimized for two task-specific
datasets and two update loops in meta learning. This paper provides a
high-performance framework for large-scale training for Optimization-based Meta
DLRM models over the \textbf{G}PU cluster, namely \textbf{G}-Meta. Firstly,
G-Meta utilizes both data parallelism and model parallelism with careful
orchestration regarding computation and communication efficiency, to enable
high-speed distributed training. Secondly, it proposes a Meta-IO pipeline for
efficient data ingestion to alleviate the I/O bottleneck. Various experimental
results show that G-Meta achieves notable training speed without loss of
statistical performance. Since early 2022, G-Meta has been deployed in Alipay's
core advertising and recommender system, shrinking the continuous delivery of
models by four times. It also obtains 6.48\% improvement in Conversion Rate
(CVR) and 1.06\% increase in CPM (Cost Per Mille) in Alipay's homepage display
advertising, with the benefit of larger training samples and tasks.