Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy Applications

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Abstract

The present work investigates the modeling of preexascale input/output (I/O) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide sufficient coverage to the formulated proxy model. The non-linear analysis data production rates are quantified as a function of a set of input parameters such as output frequency, grid size, number of levels, and the Courant-Friedrichs-Lewy (CFL) condition number for each rank, mesh level and simulation time step. Linear regression is then applied to formulate a simple analytical model which allows to translate AMReX inputs into MACSio proxy I/O application parameters, resulting in a simple 'kernel' approximation for data production at each time step. Results show that MACSio can simulate actual AMReX nonlinear 'static' I/O workloads to a certain degree of confidence on the Summit supercomputer using the present methodology. The goal is to provide an initial level of understanding of AMR I/O workloads via lightweight proxy applications models to facilitate autotune data management strategies in anticipation of exascale systems.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages952-961
Number of pages10
ISBN (Electronic)9781665497473
DOIs
StatePublished - 2022
Event36th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022 - Virtual, Online, France
Duration: May 30 2022Jun 3 2022

Publication series

NameProceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022

Conference

Conference36th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022
Country/TerritoryFrance
CityVirtual, Online
Period05/30/2206/3/22

Funding

ACKNOWLEDGEMENTS 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. This research used 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. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (https://energy.gov/ downloads/doe-public-access-plan)

Keywords

  • AMR
  • HPC
  • I/O
  • MACSio
  • Proxy
  • exascale

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