  Significant investments to upgrade and construct large-scale scientific
facilities demand commensurate investments in R&D to design algorithms and
computing approaches to enable scientific and engineering breakthroughs in the
big data era. Innovative Artificial Intelligence (AI) applications have powered
transformational solutions for big data challenges in industry and technology
that now drive a multi-billion dollar industry, and which play an ever
increasing role shaping human social patterns. As AI continues to evolve into a
computing paradigm endowed with statistical and mathematical rigor, it has
become apparent that single-GPU solutions for training, validation, and testing
are no longer sufficient for computational grand challenges brought about by
scientific facilities that produce data at a rate and volume that outstrip the
computing capabilities of available cyberinfrastructure platforms. This
realization has been driving the confluence of AI and high performance
computing (HPC) to reduce time-to-insight, and to enable a systematic study of
domain-inspired AI architectures and optimization schemes to enable data-driven
discovery. In this article we present a summary of recent developments in this
field, and describe specific advances that authors in this article are
spearheading to accelerate and streamline the use of HPC platforms to design
and apply accelerated AI algorithms in academia and industry.
