<span class="var-sub_title">Simulating the Weak Death of the Neutron in a Femtoscale Universe with Near-Exascale Computing</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Simulating the Weak Death of the Neutron in a Femtoscale Universe with Near-Exascale Computing


Authors: Evan Berkowitz (Forschungszentrum Juelich), M.A. Clark (Nvidia Corporation), Arjun Gambhir (Lawrence Livermore National Laboratory, Lawrence Berkeley National Laboratory), Ken McElvain (University of California, Berkeley; Lawrence Berkeley National Laboratory), Amy Nicholson (University of North Carolina), Enrico Rinaldi (RIKEN BNL Research Center, Lawrence Berkeley National Laboratory), Pavlos Vranas (Lawrence Livermore National Laboratory, Lawrence Berkeley National Laboratory), André Walker-Loud (Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory), Chia Cheng Chang (Lawrence Berkeley National Laboratory, RIKEN), Bálint Joó (Thomas Jefferson National Accelerator Facility), Thorsten Kurth (Lawrence Berkeley National Laboratory), Kostas Orginos (College of William & Mary, Thomas Jefferson National Accelerator Facility)

Abstract: The fundamental particle theory called Quantum Chromodynamics (QCD) dictates everything about protons and neutrons, from their intrinsic properties to interactions that bind them into atomic nuclei. Quantities that cannot be fully resolved through experiment, such as the neutron lifetime (whose precise value is important for the existence of light-atomic elements that make the sun shine and life possible), may be understood through numerical solutions to QCD. We directly solve QCD using Lattice Gauge Theory and calculate nuclear observables such as neutron lifetime. We have developed an improved algorithm that exponentially decreases the time-to-solution and applied it on the new CORAL supercomputers, Sierra and Summit. We use run-time autotuning to distribute GPU resources, achieving 20% performance at low node count. We also developed optimal application mapping through a job manager, which allows CPU and GPU jobs to be interleaved, yielding 15% of peak performance when deployed across large fractions of CORAL.




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