Enabling Agile Analysis of I/O Performance Data with PyDarshan-Reference-Cited by-同舟云学术

Enabling Agile Analysis of I/O Performance Data with PyDarshan

Published:2023-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
language:
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Author:

Luettgau Jakob¹^ORCID,Snyder Shane²^ORCID,Reddy Tyler³^ORCID,Awtrey Nikolaus³^ORCID,Harms Kevin²^ORCID,Bez Jean Luca⁴^ORCID,Wang Rui²^ORCID,Latham Rob²^ORCID,Carns Philip²^ORCID

Affiliation:

1. Inria, France

2. Argonne National Laboratory, United States of America

3. Los Alamos National Laboratory, United States of America

4. Lawrence Berkeley National Laboratory, United States of America

Funder

U.S. Department of Energy

U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624062.3624207

Reference38 articles.

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2. 2023. cuDF - GPU DataFrames. RAPIDS. https://github.com/rapidsai/cudf 2023. cuDF - GPU DataFrames. RAPIDS. https://github.com/rapidsai/cudf

3. 2023. Grafana: The Open and Composable Observability and Data Visualization Platform. Grafana Labs. https://github.com/grafana/grafana 2023. Grafana: The Open and Composable Observability and Data Visualization Platform. Grafana Labs. https://github.com/grafana/grafana

4. 2023. LLNL/Adiak. Lawrence Livermore National Laboratory. https://github.com/LLNL/Adiak 2023. LLNL/Adiak. Lawrence Livermore National Laboratory. https://github.com/LLNL/Adiak

5. 2023. Pola-Rs/Polars. pola-rs. https://github.com/pola-rs/polars 2023. Pola-Rs/Polars. pola-rs. https://github.com/pola-rs/polars

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