  This report provides an overview of recent work that harnesses the Big Data
Revolution and Large Scale Computing to address grand computational challenges
in Multi-Messenger Astrophysics, with a particular emphasis on real-time
discovery campaigns. Acknowledging the transdisciplinary nature of
Multi-Messenger Astrophysics, this document has been prepared by members of the
physics, astronomy, computer science, data science, software and
cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded
"Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale"
workshop, hosted at the National Center for Supercomputing Applications,
October 17-19, 2018. Highlights of this report include unanimous agreement that
it is critical to accelerate the development and deployment of novel,
signal-processing algorithms that use the synergy between artificial
intelligence (AI) and high performance computing to maximize the potential for
scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects
to realize this endeavor, namely (i) the design and exploitation of scalable
and computationally efficient AI algorithms for Multi-Messenger Astrophysics;
(ii) cyberinfrastructure requirements to numerically simulate astrophysical
sources, and to process and interpret Multi-Messenger Astrophysics data; (iii)
management of gravitational wave detections and triggers to enable
electromagnetic and astro-particle follow-ups; (iv) a vision to harness future
developments of machine and deep learning and cyberinfrastructure resources to
cope with the scale of discovery in the Big Data Era; (v) and the need to build
a community that brings domain experts together with data scientists on equal
footing to maximize and accelerate discovery in the nascent field of
Multi-Messenger Astrophysics.
