تقرير
Coffea -- Columnar Object Framework For Effective Analysis
| العنوان: | Coffea -- Columnar Object Framework For Effective Analysis |
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| المؤلفون: | Smith, Nicholas, Gray, Lindsey, Cremonesi, Matteo, Jayatilaka, Bo, Gutsche, Oliver, Hall, Allison, Pedro, Kevin, Acosta, Maria, Melo, Andrew, Belforte, Stefano, Pivarski, Jim |
| المصدر: | EPJ Web of Conferences 245, 06012 (2020) |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science High Energy Physics - Experiment |
| مصطلحات موضوعية: | Computer Science - Distributed, Parallel, and Cluster Computing, High Energy Physics - Experiment |
| الوصف: | The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions. Comment: As presented at CHEP 2019 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1051/epjconf/202024506012 |
| URL الوصول: | http://arxiv.org/abs/2008.12712 |
| رقم الأكسشن: | edsarx.2008.12712 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1051/epjconf/202024506012 |
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