Abstract
In this work, we present the Pele suite of software tools for compressible and incompressible reacting flows. The Pele suite leverages several different libraries, notably AMReX and SUNDIALS, to achieve performance portability on heterogeneous computing architectures across the supercomputing landscape. The Pele suite is comprised of PeleC, a compressible reacting flow block-structured adaptive mesh refinement solver, PeleLMeX, a low-Mach number reacting flow block-structured adaptive mesh refinement solver, PelePhysics, a library for transport, thermodynamics, finite rate chemistry, soot, spray and radiation physics. The objective of this paper is (i) to present the code development efforts necessary to achieve highly effective and scalable applications for exascale machines and (ii) to detail the performance results of the Combustion-Pele project applications on Oak Ridge National Laboratory’s Frontier. We show good weak and strong scaling results for both PeleC and PeleLMeX up to more than 50 billion cells on more than 4096 Frontier graphics processing unit nodes. We also present a capability demonstration simulation of a dual-fuel pulse compression ignition engine (six adaptive mesh refinement levels, and 60 billion cells or 2.1 trillion degrees of freedom) on Frontier, to date one of the largest simulations performed on the first exascale-class supercomputer.
| Original language | English |
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| Title of host publication | 2024 SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 |
| Editors | Michael Bader, Anshu Dubey, Bethany Lusch |
| Publisher | Society for Industrial and Applied Mathematics Publications |
| Pages | 13-25 |
| Number of pages | 13 |
| ISBN (Electronic) | 9781713893479 |
| State | Published - 2024 |
| Event | 22nd SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 - Baltimore, United States Duration: Mar 5 2024 → Mar 8 2024 |
Publication series
| Name | 2024 SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 |
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Conference
| Conference | 22nd SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 |
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| Country/Territory | United States |
| City | Baltimore |
| Period | 03/5/24 → 03/8/24 |
Funding
This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308; in part by Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525; and in part by UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE). This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. A portion of the research was performed using computational resources sponsored by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DEAC05-00OR22725. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. 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).