The Gordon Bell Prize is a prestigious award given each year to recognize outstanding achievement in high-performance computing internationally. Starting in 2023, an additional Gordon Bell Prize for Climate Modeling will be awarded every year for ten years to recognize the contributions of climate scientists and software engineers. Financial support for this $10,000 award is provided by Gordon Bell, a pioneer in high-performance and parallel computing.
Nominations are selected based on their impact and potential impact on the field of climate modeling, related fields, and wider society by applying high-performance computing to climate modeling applications. The award aims to recognize innovative parallel computing contributions toward solving the global climate crisis. Nominations are selected based on the performance and innovation in their computational methods and their contributions toward improving climate modeling and our understanding of the Earth’s climate system. The awardees will be announced at the International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing 2023 in November) and the finalist papers will be published in the International Journal of High Performance Computing Applications (IJHPCA).
The E3SM team submitted their work on an efficient and performance-portable implementation of the Simple Cloud Resolving E3SM Atmosphere Model (SCREAM). The SCREAM team presented performance results from Frontier, the first exascale computer in the Top500 (TOP500 Supercomputers | June 2023) list of the world’s fastest supercomputers. Importantly, the team benchmarked the model configuration they use for scientific research in contrast to a synthetic problem designed purely to demonstrate scaling. An example from one of these simulations is shown in Figure 1. The paper outlines results which to the team’s knowledge represent several firsts for a global cloud-resolving model (GCRM):
- First GCRM to run on an Exascale supercomputer,
- First GCRM to run at scale on both NVIDIA and AMD GPU systems,
- First nonhydrostatic GCRM to exceed 1 simulated-year-per-day (SYPD) of model throughput.
Building on the work detailed in an earlier article (Exascale Performance of SCREAM), the E3SM team has updated its finalist submission with new performance results. In the months since the original submission, the scientists were able to significantly improve the performance at scale on the Frontier system culminating in a 54% full-model improvement at 8,192 nodes (45% improvement for atmosphere throughput) and surpassing 1 SYPD for the first time for the full model.
The performance of SCREAM v1 running in a 3.25 km configuration is shown in Figure 2. The left panel depicts the performance of the full model running with prescribed sea surface temperature (SST). This includes the cost of the land, sea ice, and data ocean components, as well as the cost of communicating the fluxes and other state variables between these components and the atmosphere. These component and data models are relatively inexpensive and run 10-20 times faster than the atmosphere, and result in an increased cost on Frontier of the full model as compared to the atmosphere component (right panel, Fig 1.) ranging from 14% (on 512 nodes) to 21% (on 8,192 nodes). At higher node counts, the cost of the communication between the components becomes more significant, resulting in slightly less scalability for the full model as compared to the atmosphere component. E3SM’s best results are obtained on 8192 nodes, where the full model obtains 1.26 SYPD with the atmosphere component running at 1.52 SYPD.
- Mark A. Taylor (SNL), Peter M. Caldwell (LLNL), Luca Bertagna (SNL), Conrad Clevenger (SNL), Aaron S. Donahue (LLNL), James G. Foucar (SNL), Oksana Guba (SNL), Benjamin R. Hillman (SNL), Noel Keen (LBNL), Jayesh Krishna (ANL), Matthew R. Norman (ORNL), Sarat Sreepathi (ORNL), Christopher R. Terai (LLNL), James B. White III (HPE), Danqing Wu (ANL), Andrew G. Salinger (SNL), Renata B. McCoy (LLNL), L. Ruby Leung (PNNL), David C. Bader (LLNL), The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System, Submission to the ACM Gordon Bell Prize for Climate Modeling, 2023.
- Mark Taylor, Sandia National Laboratories