Gordon Bell Award Finalists
Large-scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys
Sambit Das, Bikash Kanungo, Vishal Subramanian, and others (eight authors total) as part of a team that includes the University of Michigan, Indian Institute of Science, and Oak Ridge National Laboratory
In this work, the team developed a mixed method to combine density function theory (DFT) and the quantum many body (QMB) problem using a machine learning technique. The effort achieves high accuracy of calculation and affords large-scale modeling with the inverse-DFT that links the QMB method to DFT. They realized the ground-stage energy calculation while keeping the accuracy commensurate with QMB, using more than 60% of resources on the Frontier supercomputer housed within the Oak Ridge Leadership Computing Facility.
Exascale Multiphysics Nuclear Reactor Simulations for Advanced Designs
Elia Merzaria, Steven Hamilton, Thomas Evans, and others (12 authors total) featuring a team from Pennsylvania State University, Oak Ridge National Laboratory, Argonne National Laboratory, and University of Illinois at Urbana-Champaign
The team simulated an advanced nuclear reactor system coupling radiation transport with heat and fluid simulation, including the high-fidelity, high-resolution Monte-Carlo code, Shift, and the computational fluid dynamics code, NekRS. Nek5000/RS was implemented on ORNL’s Frontier system and achieved 1 billion spectral elements and 350 billion degrees of freedom, while Shift achieved very high weak-scaling on 8192 system nodes. As a result, they calculated six reactions in 214,896 fuel pin regions below 1% statistical error, yielding first-of-a-kind resolution for a Monte Carlo transport application.
Scaling the Leading Accuracy of Deep Equivariant Models to Biomolecular Simulations of Realistic Size
Albert Musaelian, Anders Johansson, Simon Batzner, and Boris Kozinsky as part of a team from the Harvard John A. Paulson School of Engineering and Applied Sciences
The group developed the Allegro architecture to bridge the accuracy-speed tradeoff of atomistic simulations and enable the description of dynamics in structures of unprecedented complexity at quantum fidelity. This is achieved through a combination of innovative model architecture, massive parallelization, and model implementations optimized for efficient GPU utilization. Allegro’s scalability is illustrated by a nanoseconds-long stable simulation of protein dynamics and up to 44-million atom structure of a complete, all-atom, explicitly solvated HIV capsid on the Perlmutter system at the National Energy Research Scientific Computing Center. They achieved strong scaling up to 100 million atoms.
See the full list of 2023 Gordon Bell finalists [HERE].
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Gordon Bell Prize for Climate Modeling
The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System
Authors: Mark Taylor, Peter M. Caldwell, Luca Bertagna, Conrad Clevenger, Aaron S. Donahue, James G. Foucar, Oksana Guba, Benjamin R. Hillman, Noel Keen, Jayesh Krishna, Matthew R. Norman, Sarat Sreepathi, Christopher R. Terai, James B. White III, Danqing Wu, Andrew G. Salinger, Renata B. McCoy, L. Ruby Leung, and David C. Bader
This work introduces an efficient and performance portable implementation of the Simple Cloud Resolving E3SM Atmosphere Model (SCREAM). SCREAM is a full-featured atmospheric global circulation cloud-resolving model. A significant advancement is SCREAM was developed anew using C++ and incorporates the Kokkos library to abstract the on-node execution model for both CPUs and GPUs. To date, only a few global atmosphere models have been ported to GPUs. SCREAM was able to run on both AMD and NVIDIA GPUs and on nearly an entire exascale system (Frontier). On the Frontier system, it achieved a groundbreaking performance, simulating 1.26 years per day for a practical cloud-resolving simulation. This constitutes a pivotal stride in climate modeling, offering enhanced and highly necessary predictions regarding the potential outcomes of future climate changes.
See the full list of 2023 Gordon Bell Prize for Climate Modeling finalists [HERE].
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