The present work investigates the modeling of pre-exascale 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 non-linear "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.
@article{arxiv.2206.00108,
title = {Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy Applications},
author = {William F Godoy and Jenna Delozier and Gregory R Watson},
journal= {arXiv preprint arXiv:2206.00108},
year = {2023}
}
Comments
10 pages, 11 figures, accepted at Seventeenth International Workshop on Automatic Performance Tuning, iWAPT2022, held in conjunction with IEEE IPDPS 2022