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A Faster, More Intuitive RooFit
arXiv - CS - Mathematical Software Pub Date : 2020-03-28 , DOI: arxiv-2003.12875
Stephan Hageboeck

RooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider as well as at $B$ factories. Larger datasets to be collected at e.g. the High-Luminosity LHC will enable measurements with higher precision, but will require faster data processing to keep fitting times stable. In this work, a simplification of RooFit's interfaces and a redesign of its internal dataflow is presented. Interfaces are being extended to look and feel more STL-like to be more accessible both from C++ and Python to improve interoperability and ease of use, while maintaining compatibility with old code. The redesign of the dataflow improves cache locality and data loading, and can be used to process batches of data with vectorised SIMD computations. This reduces the time for computing unbinned likelihoods by a factor four to 16. This will allow to fit larger datasets of the future in the same time or faster than today's fits.

中文翻译:

更快、更直观的 RooFit

RooFit 和 RooStats 是 ROOT 中的统计建模工具包,用于大型强子对撞机和 $B$ 工厂的大多数搜索和测量。将在例如高亮度 LHC 上收集的更大数据集将实现更高精度的测量,但需要更快的数据处理以保持拟合时间稳定。在这项工作中,提出了 RooFit 界面的简化及其内部数据流的重新设计。接口被扩展为看起来和感觉更像 STL,以便更容易从 C++ 和 Python 访问,以提高互操作性和易用性,同时保持与旧代码的兼容性。数据流的重新设计改进了缓存局部性和数据加载,并可用于通过矢量化 SIMD 计算处理批量数据。
更新日期:2020-07-28
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