%0 Conference Paper
%A Zhu, Zhaobin
%A Neuwirth, Sarah
%A Lippert, Thomas
%T A Comprehensive I/O Knowledge Cycle for Modular and Automated HPC Workload Analysis
%I IEEE
%M FZJ-2023-01736
%P 581-588
%D 2022
%X 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.
%B 2022 IEEE International Conference on Cluster Computing (CLUSTER)
%C 5 Sep 2022 - 8 Sep 2022, Heidelberg (Germany)
Y2 5 Sep 2022 - 8 Sep 2022
M2 Heidelberg, Germany
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%U <Go to ISI:>//WOS:000920273100061
%R 10.1109/CLUSTER51413.2022.00076
%U https://juser.fz-juelich.de/record/1006595