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  A Fast MPI-Based Distributed Hash-Table as Surrogate Model for HPC Applications

Lübke, M., De Lucia, M., Petri, S., Schnor, B. (2025): A Fast MPI-Based Distributed Hash-Table as Surrogate Model for HPC Applications. - In: Lees, M. H., Cai, W., Ann, C. S., Yi, S., Abramson, D., Dongarra, J. J., Sloot, P. M. A. (Eds.), - Computational Science – ICCS 2025: 25th International Conference, Singapore, July 7-9, 2025, Proceedings, (Lecture Notes in Computer Science ; 15906), 233-240.
https://doi.org/10.1007/978-3-031-97635-3_28

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_32218 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_32218_4
Genre: Conference Paper

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https://doi.org/10.48550/arXiv.2504.14374 (Preprint)
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https://doi.org/10.5281/zenodo.15235554 (Supplementary material)
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This directory contains the source code for the MPI-based DHT (Distributed Hash Table) implementation and benchmarks for reproducibility purposes.
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 Creators:
Lübke, Max1, Author
De Lucia, Marco2, Author
Petri, Stefan1, Author                 
Schnor, Bettina2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Surrogate models can enhance performance in HPC applications. Cache-based surrogates use pre-calculated simulation results for interpolation or extrapolation. However, this is only effective if retrieval is faster than simulation. This paper proposes an MPI-based distributed architecture where parallel processes share memory to build a distributed hash table. Three DHT approaches for HPC are presented. A lock-free design outperforms both coarse-grained and fine-grained locking DHTs, demonstrating good scaling for read and write performance. The lock-free DHT improved the runtime of a reactive transport simulation up to 42%.

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Language(s): eng - English
 Dates: 2025-04-302025-07-062025-07-06
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: PIKDOMAIN: RD1 - Earth System Analysis
Organisational keyword: RD1 - Earth System Analysis
Working Group: Past and Future Earth
Research topic keyword: Energy
Model / method: Machine Learning
Model / method: Open Source Software
MDB-ID: No MDB - stored outside PIK (see locators/paper)
OATYPE: Green Open Access
DOI: 10.1007/978-3-031-97635-3_28
 Degree: -

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Title: Computational Science – ICCS 2025: 25th International Conference, Singapore, July 7-9, 2025, Proceedings
Source Genre: Proceedings
 Creator(s):
Lees, M. H.1, Editor
Cai, W.1, Editor
Ann, C. S.1, Editor
Yi, S.1, Editor
Abramson, D.1, Editor
Dongarra, J. J.1, Editor
Sloot, P. M. A.1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Cham : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 233 - 240 Identifier: DOI: 10.1007/978-3-031-97635-3
ISBN: 978-3-031-97635-3

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 15906 Sequence Number: - Start / End Page: - Identifier: -