A Faster, More Intuitive RooFit
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 HighLuminosity LHC will enable measurements with higher precision, but will require fast...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2020-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06007.pdf |
| _version_ | 1818690627084222464 |
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| author | Hageböck Stephan |
| author_facet | Hageböck Stephan |
| author_sort | Hageböck Stephan |
| collection | DOAJ |
| description | 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 HighLuminosity 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. |
| first_indexed | 2024-12-17T12:29:00Z |
| format | Article |
| id | doaj.art-38e2f35a1c384815a5531b3d2aea50e3 |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-12-17T12:29:00Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-38e2f35a1c384815a5531b3d2aea50e32022-12-21T21:48:40ZengEDP SciencesEPJ Web of Conferences2100-014X2020-01-012450600710.1051/epjconf/202024506007epjconf_chep2020_06007A Faster, More Intuitive RooFitHageböck Stephan0CERNRooFit 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 HighLuminosity 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.https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06007.pdf |
| spellingShingle | Hageböck Stephan A Faster, More Intuitive RooFit EPJ Web of Conferences |
| title | A Faster, More Intuitive RooFit |
| title_full | A Faster, More Intuitive RooFit |
| title_fullStr | A Faster, More Intuitive RooFit |
| title_full_unstemmed | A Faster, More Intuitive RooFit |
| title_short | A Faster, More Intuitive RooFit |
| title_sort | faster more intuitive roofit |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06007.pdf |
| work_keys_str_mv | AT hagebockstephan afastermoreintuitiveroofit AT hagebockstephan fastermoreintuitiveroofit |