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UID:submissions.pasc-conference.org_PASC24_sess179_pap121@linklings.com
SUMMARY:Reducing the Impact of I/O Contention in Numerical Weather Predict
 ion Workflows at Scale Using DAOS
DESCRIPTION:Paper\n\nNicolau Manubens Gil (ECMWF, EPCC); Simon D. Smart, E
 manuele Danovaro, and Tiago Quintino (ECMWF); and Adrian Jackson (EPCC)\n\
 nOperational Numerical Weather Prediction (NWP) workflows are highly data-
 intensive. Data volumes have increased by many orders of magnitude over th
 e last 40 years, and are expected to continue to do so, especially given t
 he upcoming adoption of Machine Learning in forecast processes. Parallel P
 OSIX-compliant file systems have been the dominant paradigm in data storag
 e and exchange in HPC workflows for many years. This paper presents ECMWF'
 s move beyond the POSIX paradigm, implementing a backend for their storage
  library to support DAOS --- a novel high-performance object store designe
 d for massively distributed Non-Volatile Memory. This system is demonstrat
 ed to be able to outperform the highly mature and optimised POSIX backend 
 when used under high load and contention, as per typical forecast workflow
  I/O patterns. This work constitutes a significant step forward, beyond th
 e performance constraints imposed by POSIX semantics.\n\nDomain: Climate, 
 Weather, and Earth Sciences, Computational Methods and Applied Mathematics
 \n\nSession Chair: Thorsten Kurth (NVIDIA Inc.)
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