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@INPROCEEDINGS{Zhu:1006595,
author = {Zhu, Zhaobin and Neuwirth, Sarah and Lippert, Thomas},
title = {{A} {C}omprehensive {I}/{O} {K}nowledge {C}ycle for
{M}odular and {A}utomated {HPC} {W}orkload {A}nalysis},
publisher = {IEEE},
reportid = {FZJ-2023-01736},
pages = {581-588},
year = {2022},
abstract = {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.},
month = {Sep},
date = {2022-09-05},
organization = {2022 IEEE International Conference on
Cluster Computing (CLUSTER), Heidelberg
(Germany), 5 Sep 2022 - 8 Sep 2022},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5121 - Supercomputing $\&$ Big Data Facilities (POF4-512)},
pid = {G:(DE-HGF)POF4-5121},
typ = {PUB:(DE-HGF)8},
UT = {WOS:000920273100061},
doi = {10.1109/CLUSTER51413.2022.00076},
url = {https://juser.fz-juelich.de/record/1006595},
}