论文标题
更快，更直观的屋顶
A Faster, More Intuitive RooFit
论文作者
论文摘要
Roofit和Roostats（在根中的大多数搜索和测量）以及$ B $工厂的大多数搜索和测量中都使用了统计建模工具包。较大的数据集将在例如高光度LHC将以更高的精度启用测量，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，介绍了屋顶界面的简化及其内部数据流的重新设计。正在扩展接口，以使外观和感觉更像STL，从C ++和Python更容易访问，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善了缓存位置和数据加载，可用于使用矢量化的SIMD计算来处理数据批次。这将计算无界可能性计算的时间减少了四到16个因素。这将允许在同一时间或比今天的拟合中适应未来的更大数据集或更快的速度。
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.
