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@MASTERSTHESIS{Djebarov:995779,
author = {Djebarov, Dlyaver},
othercontributors = {Müller, Matthias S. and Neuwirth, Sarah and Liem, Radita
Tapaning Hesti},
title = {{E}xtending h5bench with {I}/{O} access patterns in common
{AI} applications},
school = {RWTH Aachen University},
type = {Bachelorarbeit},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-10149},
pages = {1 Online-Ressource: Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Bachelorarbeit, RWTH Aachen University, 2024},
abstract = {Rapid artificial intelligence (AI) adoption in scientific
computing requires new tools to evaluate I/O performance
effectively. HDF5 is one of the data formats commonly used
not only in HPC applications but also in modern AI
applications. However, the existing benchmarks are
insufficient to address the current challenges posed by AI
workloads. This thesis introduces an extension to the
existing HDF5 benchmark, called h5bench, by incorporating
the same workload from the MLPerf Storage - DLIO Benchmark.
This extension allows users to test AI workloads without the
need to install machine learning libraries, reducing
complexity and enhancing the usability of the benchmark. The
experimental analysis demonstrates that the extension
managed to replicate the existing I/O patterns with
easy-to-adjust configurations to perform various scalability
tests.},
cin = {123010 / 120000 / 022000},
ddc = {004},
cid = {$I:(DE-82)123010_20140620$ / $I:(DE-82)120000_20140620$ /
$I:(DE-82)022000_20140101$},
typ = {PUB:(DE-HGF)2},
doi = {10.18154/RWTH-2024-10149},
url = {https://publications.rwth-aachen.de/record/995779},
}
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