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Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization

  • Benjamin Wilfong
  • , Anand Radhakrishnan
  • , Henry Le Berre
  • , Daniel Vickers
  • , Tanush Prathi
  • , Nikolaos Tselepidis
  • , Benedikt Dorschner
  • , Reuben Budiardja
  • , Brian Cornille
  • , Stephen Abbott
  • , Florian Schäfer
  • , Spencer Bryngelson

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

2 Scopus citations

Abstract

We present an optimized implementation of the recently proposed information geometric regularization (IGR) for unprecedented scale simulation of compressible fluid flows applied to multi-engine spacecraft boosters. We improve upon state-of-the-art computational fluid dynamics (CFD) techniques in terms of computational cost, memory footprint, and energy-to-solution metrics. Unified memory on coupled CPU-GPU or APU platforms increases problem size with negligible overhead. Mixed half/single-precision storage and computation are used on well-conditioned numerics. We simulate flow at 200 trillion grid points and 1 quadrillion degrees of freedom, exceeding the current record by a factor of 20. A factor of 4 wall-time speedup is achieved over optimized baselines. Ideal weak scaling is observed on OLCF Frontier, LLNL El Capitan, and CSCS Alps using the full systems. Strong scaling is near ideal at extreme conditions, including 80% efficiency on CSCS Alps with an 8 node baseline and stretching to the full system.

Original languageEnglish
Title of host publicationProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025
PublisherAssociation for Computing Machinery, Inc
Pages14-24
Number of pages11
ISBN (Electronic)9798400714665
DOIs
StatePublished - Nov 15 2025
Event2025 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025 - St. Louis, United States
Duration: Nov 16 2025Nov 21 2025

Publication series

NameProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025

Conference

Conference2025 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2025
Country/TerritoryUnited States
CitySt. Louis
Period11/16/2511/21/25

Funding

SHB acknowledges the use of resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 and allocation CFD154 (PI Bryngelson). This work was also supported by a grant from the Swiss National Supercomputing Centre (CSCS) for Alps. FS acknowledges support from the Air Force Office of Scientific Research under award number FA9550-23-1-0668 (Information Geometric Regularization for Simulation and Optimization of Supersonic Flow). The authors gratefully acknowledge contributions from Scott Futral (LLNL), Rob Noska (HPE), Michael Sandoval (OLCF), and Mat Col-grove (NVIDIA).

Keywords

  • CFD
  • exascale
  • regularization
  • unified memory

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