  During the last two years, the goal of many researchers has been to squeeze
the last bit of performance out of HPC system for AI tasks. Often this
discussion is held in the context of how fast ResNet50 can be trained.
Unfortunately, ResNet50 is no longer a representative workload in 2020. Thus,
we focus on Recommender Systems which account for most of the AI cycles in
cloud computing centers. More specifically, we focus on Facebook's DLRM
benchmark. By enabling it to run on latest CPU hardware and software tailored
for HPC, we are able to achieve more than two-orders of magnitude improvement
in performance (110x) on a single socket compared to the reference CPU
implementation, and high scaling efficiency up to 64 sockets, while fitting
ultra-large datasets. This paper discusses the optimization techniques for the
various operators in DLRM and which component of the systems are stressed by
these different operators. The presented techniques are applicable to a broader
set of DL workloads that pose the same scaling challenges/characteristics as
DLRM.
