h1

TY  - THES
AU  - Djebarov, Dlyaver
TI  - Extending h5bench with I/O access patterns in common AI applications
PB  - RWTH Aachen University
VL  - Bachelorarbeit
CY  - Aachen
M1  - RWTH-2024-10149
SP  - 1 Online-Ressource: Illustrationen
PY  - 2024
N1  - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University
N1  - Bachelorarbeit, RWTH Aachen University, 2024
AB  - 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.
LB  - PUB:(DE-HGF)2
DO  - DOI:10.18154/RWTH-2024-10149
UR  - https://publications.rwth-aachen.de/record/995779
ER  -