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001006595 037__ $$aFZJ-2023-01736
001006595 1001_ $$0P:(DE-HGF)0$$aZhu, Zhaobin$$b0
001006595 1112_ $$a2022 IEEE International Conference on Cluster Computing (CLUSTER)$$cHeidelberg$$d2022-09-05 - 2022-09-08$$wGermany
001006595 245__ $$aA Comprehensive I/O Knowledge Cycle for Modular and Automated HPC Workload Analysis
001006595 260__ $$bIEEE$$c2022
001006595 300__ $$a581-588
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001006595 520__ $$aOn the way to the exascale era, millions of parallel processing elements are required. Accordingly, one major chal-lenge is the ever-widening gap between computational power and underlying I/O systems. To bridge this gap, I/O resources must be used efficiently, thus a profound I/O knowledge is required. In this work, we analyze state-of-the-art approaches that can be applied to improve the general I/O understanding and performance. Based on our analysis, we present an automated, modular, tool-agnostic I/Oanalysis workflow and a prototype implementation that can be used to generate, extract, store, analyze, and use I/O knowledge in a structured and reproducible way.
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001006595 7001_ $$0P:(DE-Juel1)188610$$aNeuwirth, Sarah$$b1$$eCorresponding author
001006595 7001_ $$0P:(DE-Juel1)132179$$aLippert, Thomas$$b2
001006595 773__ $$a10.1109/CLUSTER51413.2022.00076
001006595 8564_ $$uhttps://juser.fz-juelich.de/record/1006595/files/2022-REXIO-Zhu-camera.pdf$$yOpenAccess
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001006595 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132179$$aForschungszentrum Jülich$$b2$$kFZJ
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001006595 9141_ $$y2023
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