  The use of GPUs has proliferated for machine learning workflows and is now
considered mainstream for many deep learning models. Meanwhile, when training
state-of-the-art personal recommendation models, which consume the highest
number of compute cycles at our large-scale datacenters, the use of GPUs came
with various challenges due to having both compute-intensive and
memory-intensive components. GPU performance and efficiency of these
recommendation models are largely affected by model architecture configurations
such as dense and sparse features, MLP dimensions. Furthermore, these models
often contain large embedding tables that do not fit into limited GPU memory.
The goal of this paper is to explain the intricacies of using GPUs for training
recommendation models, factors affecting hardware efficiency at scale, and
learnings from a new scale-up GPU server design, Zion.
