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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.15919 |
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| _version_ | 1866918525728194560 |
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| author | Vogelsang, Jan Lober, Melissa Schöfmann, Catherine Mia Villamar, José Terhorst, Dennis Senk, Johanna Plesser, Hans Ekkehard Diesmann, Markus Kunkel, Susanne Kurth, Anno C. |
| author_facet | Vogelsang, Jan Lober, Melissa Schöfmann, Catherine Mia Villamar, José Terhorst, Dennis Senk, Johanna Plesser, Hans Ekkehard Diesmann, Markus Kunkel, Susanne Kurth, Anno C. |
| contents | Drawing on ideas from continuous integration, we present concepts of an automated benchmarking pipeline for high performance applications. Customization and collaboration have been key design goals owing to the requirements of research-software development as a continuous community effort. We have extended our previous conceptual work on systematic benchmarking workflows with the functionality of user-agnostic operations as well as continuous benchmarking. This fosters reproducibility and re-use of benchmarking results to ensure sustainable technological progress. We provide software-engineering solutions to keep pace with the rapid evolution of both large-scale models and high-performance computing systems with a view towards the scientific domains of neuroscience and artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15919 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies Vogelsang, Jan Lober, Melissa Schöfmann, Catherine Mia Villamar, José Terhorst, Dennis Senk, Johanna Plesser, Hans Ekkehard Diesmann, Markus Kunkel, Susanne Kurth, Anno C. Distributed, Parallel, and Cluster Computing Drawing on ideas from continuous integration, we present concepts of an automated benchmarking pipeline for high performance applications. Customization and collaboration have been key design goals owing to the requirements of research-software development as a continuous community effort. We have extended our previous conceptual work on systematic benchmarking workflows with the functionality of user-agnostic operations as well as continuous benchmarking. This fosters reproducibility and re-use of benchmarking results to ensure sustainable technological progress. We provide software-engineering solutions to keep pace with the rapid evolution of both large-scale models and high-performance computing systems with a view towards the scientific domains of neuroscience and artificial intelligence. |
| title | Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2604.15919 |