Abstract. The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of highcapacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

Imagine being able to predict -with unprecedented accuracy and precision -the structure of the proton and neutron, and the forces between them, directly from the dynamics of quarks and gluons, and then using this information in calculations of the structure and reactions of atomic nuclei and of the properties of dense neutron stars (NSs). Also imagine discovering new and exotic states of matter, and new laws of nature, by being able to collect more experimental data than we dream possible today, analyzing it in real time to feed back into an experiment, and curating the data with full tracking capabilities and with fully distributed data mining capabilities. Making this vision a reality would improve basic scientific understanding, enabling us to precisely calculate, for example, the spectrum of gravity waves emitted during NS coalescence, and would have important societal applications in nuclear energy research, stockpile stewardship, and other areas. This review presents the components and characteristics of the exascale computing ecosystems necessary to realize this vision.Nuclear physics research and its applications, more than many other areas of science, rely heavily on large-scale, high-performance computing (HPC). HPC is integral to (1) the design and optimization of an extensive and vibrant experimental program, (2) acquisition and handling of large volumes of experimental data, and (3) large-scale simulations of emergent complex systems -from the subatomic to the cosmological. Dramatic progress has already been made in enhancing our understanding of strongly-interacting matter across many areas of physics: from the quark and gluon structure of the proton and neutron, the building blocks of atomic nuclei; to relating the hot dense plasma in the early universe to the near-perfect fluid produced at the Relativistic Heavy Ion Collider (RHIC); to the structure of rare isotopes and the reactions required to form all the elements of the universe; to the creation and properties of the dense cold matter formed in supernovae and NSs.In addition to pursuing new analytical and experimental techniques and algorithms and more complete physical models at higher resolution, the following broadly grouped areas relevant to the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) and the National Science Foundation (NSF) would directly affect the mission need of the DOE Office of Science (SC) Nuclear Physics (NP) program.Exascale capability computing and associated capacity computing will revolutionize our understanding of nuclear physics and nuclear applications.Closely tied to the needs for exascale hardware are the requirements for new software (codes, algorithms, and workflows) appropriate for the exascale ecosystem, which can be developed through collaborations among ASCR and NSF mathematicians and computer scientists and NP scientists.

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