  The widespread application of deep learning has changed the landscape of
computation in the data center. In particular, personalized recommendation for
content ranking is now largely accomplished leveraging deep neural networks.
However, despite the importance of these models and the amount of compute
cycles they consume, relatively little research attention has been devoted to
systems for recommendation. To facilitate research and to advance the
understanding of these workloads, this paper presents a set of real-world,
production-scale DNNs for personalized recommendation coupled with relevant
performance metrics for evaluation. In addition to releasing a set of
open-source workloads, we conduct in-depth analysis that underpins future
system design and optimization for at-scale recommendation: Inference latency
varies by 60% across three Intel server generations, batching and co-location
of inferences can drastically improve latency-bounded throughput, and the
diverse composition of recommendation models leads to different optimization
strategies.
