High-Performance Data Format for Scientific Data Storage and Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915402178625536 |
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| author | Gavalian, Gagik |
| author_facet | Gavalian, Gagik |
| contents | In this article, we present the High-Performance Output (HiPO) data format developed at Jefferson Laboratory for storing and analyzing data from Nuclear Physics experiments. The format was designed to efficiently store large amounts of experimental data, utilizing modern fast compression algorithms. The purpose of this development was to provide organized data in the output, facilitating access to relevant information within the large data files. The HiPO data format has features that are suited for storing raw detector data, reconstruction data, and the final physics analysis data efficiently, eliminating the need to do data conversions through the lifecycle of experimental data. The HiPO data format is implemented in C++ and JAVA, and provides bindings to FORTRAN, Python, and Julia, providing users with the choice of data analysis frameworks to use. In this paper, we will present the general design and functionalities of the HiPO library and compare the performance of the library with more established data formats used in data analysis in High Energy and Nuclear Physics (such as ROOT and Parquete). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07666 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Performance Data Format for Scientific Data Storage and Analysis Gavalian, Gagik Data Analysis, Statistics and Probability In this article, we present the High-Performance Output (HiPO) data format developed at Jefferson Laboratory for storing and analyzing data from Nuclear Physics experiments. The format was designed to efficiently store large amounts of experimental data, utilizing modern fast compression algorithms. The purpose of this development was to provide organized data in the output, facilitating access to relevant information within the large data files. The HiPO data format has features that are suited for storing raw detector data, reconstruction data, and the final physics analysis data efficiently, eliminating the need to do data conversions through the lifecycle of experimental data. The HiPO data format is implemented in C++ and JAVA, and provides bindings to FORTRAN, Python, and Julia, providing users with the choice of data analysis frameworks to use. In this paper, we will present the general design and functionalities of the HiPO library and compare the performance of the library with more established data formats used in data analysis in High Energy and Nuclear Physics (such as ROOT and Parquete). |
| title | High-Performance Data Format for Scientific Data Storage and Analysis |
| topic | Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2501.07666 |