Frontier: Exploring Exascale The System Architecture of the First Exascale Supercomputer

Scott Atchley, Chris Zimmer, John R. Lange, David E. Bernholdt, Veronica G. Melesse Vergara, Thomas Beck, Michael J. Brim, Reuben Budiardja, Sunita Chandrasekaran, Markus Eisenbach, Thomas Evans, Matthew Ezell, Nicholas Frontiere, Antigoni Georgiadou, Joe Glenski, Philipp Grete, Steven Hamilton, John Holmen, Axel Huebl, Daniel JacobsonWayne Joubert, Kim McMahon, Elia Merzari, Stan G. Moore, Andrew Myers, Stephen Nichols, Sarp Oral, Thomas Papatheodore, Danny Perez, David M. Rogers, Evan Schneider, Jean Luc Vay, P. K. Yeung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

As the US Department of Energy (DOE) computing facilities began deploying petascale systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA published a report on the feasibility of reaching exascale. The report authors identified several key challenges in the pursuit of exascale including power, memory, concurrency, and resiliency. That report informed the DOE's computing strategy for reaching exascale. With the deployment of Oak Ridge National Laboratory's Frontier supercomputer, we have officially entered the exascale era. In this paper, we discuss Frontier's architecture, how it addresses those challenges, and describe some early application results from Oak Ridge Leadership Computing Facility's Center of Excellence and the Exascale Computing Project.

Original languageEnglish
Title of host publicationSC 2023 - International Conference for High Performance Computing, Networking, Storage and Analysis
PublisherIEEE Computer Society
ISBN (Electronic)9798400701092
DOIs
StatePublished - 2023
Event2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 - Denver, United States
Duration: Nov 12 2023Nov 17 2023

Publication series

NameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
ISSN (Print)2167-4329
ISSN (Electronic)2167-4337

Conference

Conference2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023
Country/TerritoryUnited States
CityDenver
Period11/12/2311/17/23

Funding

The authors gratefully acknowledge our colleagues and collaborators at ORNL, HPE, and AMD. Deploying leadership HPC systems requires an army of dedicated professionals and we are privileged to be able to work with them. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). We acknowledge all WarpX contributors, which are primarily with LBNL, LLNL, CEA-LIDYL, SLAC, DESY, CERN, and TAE. Luca Fedeli and Henri Vincenti contributed significantly to the presented Frontier results together with co-authors of [16]. WarpX co-authors want to highlight the leading contributions of Weiqun Zhang, Remi Lehe, Maxence Thevenet. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101030214.

FundersFunder number
High Energy Physics
DOE Public Access Plan
Advanced Scientific Computing Research
U.S. Department of Energy
Office of Science
CERN
Office of Science and National Nuclear Security Administration
Horizon 2020
Oak Ridge National LaboratoryDE-AC05-00OR22725
Laboratory Directed Research and DevelopmentDE-AC02-05CH11231
H2020 Marie Skłodowska-Curie Actions101030214
TAE17-SC-20-SC
Lawrence Livermore National LaboratoryDE-AC52-07NA27344

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