@inproceedings{ec41fbaa75c04db9afdfc3c5a2601914,
title = "I/O in WRF: A Case Study in Modern Parallel I/O Techniques",
abstract = "Large-scale parallel applications can face significant I/O performance bottlenecks, making efficient I/O crucial. This work presents a comparative study of several parallel I/O implementations in the Weather Research and Forecasting model, including PnetCDF blocking and non-blocking I/O options, netCDF4, HDF5 Log VOL, and ADIOS. For I/O methods creating files in a canonical data layout, PnetCDF's non-blocking option offers up to 2x improvement over its blocking option and up to 4.5x over HDF5 via netCDF4, demonstrating the effectiveness of the write request aggregation technique. The HDF5 Log VOL outperforms ADIOS with a 4x improvement in write performance when creating files in the log layout, although both require non-negligible time to convert the file back to canoni-cal order for post-run analysis. From these results we extract some observations that can guide I/O strategies for modern parallel codes.",
keywords = "I/O performance tuning, Parallel I/O, WRF, benchmarking",
author = "Zanhua Huang and Kaiyuan Hou and Ankit Agrawal and Alok Choudhary and Robert Ross and Liao, {Wei Keng}",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 ; Conference date: 12-11-2023 Through 17-11-2023",
year = "2023",
doi = "10.1145/3581784.3613216",
language = "English (US)",
series = "International Conference for High Performance Computing, Networking, Storage and Analysis, SC",
publisher = "IEEE Computer Society",
booktitle = "SC 2023 - International Conference for High Performance Computing, Networking, Storage and Analysis",
address = "United States",
}