Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
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|
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Article Number | 04012 | |
Number of page(s) | 8 | |
Section | Distributed Computing | |
DOI | https://doi.org/10.1051/epjconf/202429504012 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429504012
CernVM-FS at Extreme Scales
1 CERN, Switzerland
2 Jump Trading, LLC
* e-mail: laura.promberger@cern.ch
Published online: 6 May 2024
The CernVM File System (CVMFS) provides the software distribution backbone for High Energy and Nuclear Physics experiments and many other scientific communities in the form of a globally available shared software area. It has been designed for the software distribution problem of experiment software for LHC Runs 1 and 2. For LHC Run 3 and even more so for HL-LHC (Runs 4-6), the complexity of the experiment software stacks and their build pipelines is substantially larger. For instance, software is being distributed for several CPU architectures, often in the form of containers which includes base and operating system libraries, the number of external packages such as machine learning libraries has multiplied, and there is a shift from C++ to more Python-heavy software stacks that results in more and smaller files needing to be distributed. For CVMFS, the new software landscape means an order of magnitude increase of scale in several key metrics. This contribution reports on the performance and reliability engineering on the file system client to sustain current and expected future software access load. Concretely, the impact of the newly designed file system cache management is shown, including significant performance improvements for HEP-representative benchmark workloads, and an up to 25% performance increase in software built-time when the build tools reside on CVMFS. Operational improvements presented include better network failure handling, error reporting, and integration with container runtimes. And a pilot study using zstd as compression algorithm shows that it could bring significant improvements for remote data access times.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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