TY  - CONF
AU  - Zhu, Zhaobin
AU  - Neuwirth, Sarah
AU  - Lippert, Thomas
TI  - A Comprehensive I/O Knowledge Cycle for Modular and Automated HPC Workload Analysis
PB  - IEEE
M1  - FZJ-2023-01736
SP  - 581-588
PY  - 2022
AB  - On the way to the exascale era, millions of parallel processing elements are required. Accordingly, one major chal-lenge is the ever-widening gap between computational power and underlying I/O systems. To bridge this gap, I/O resources must be used efficiently, thus a profound I/O knowledge is required. In this work, we analyze state-of-the-art approaches that can be applied to improve the general I/O understanding and performance. Based on our analysis, we present an automated, modular, tool-agnostic I/Oanalysis workflow and a prototype implementation that can be used to generate, extract, store, analyze, and use I/O knowledge in a structured and reproducible way.
T2  - 2022 IEEE International Conference on Cluster Computing (CLUSTER)
CY  - 5 Sep 2022 - 8 Sep 2022, Heidelberg (Germany)
Y2  - 5 Sep 2022 - 8 Sep 2022
M2  - Heidelberg, Germany
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:000920273100061
DO  - DOI:10.1109/CLUSTER51413.2022.00076
UR  - https://juser.fz-juelich.de/record/1006595
ER  -