Autor: |
Wolffs Zef, Bos Patrick, Brenner Lydia, Verkerke Wouter, van Vulpen Ivo |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
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Zdroj: |
EPJ Web of Conferences, Vol 295, p 06007 (2024) |
Druh dokumentu: |
article |
ISSN: |
2100-014X |
DOI: |
10.1051/epjconf/202429506007 |
Popis: |
In the context of High Energy Physics (HEP) analyses the advent of large-scale combination fits forms an increasing computational challenge for the underlying software frameworks on which these fits rely. RooFit, being the central tool for HEP statistical model creation and fitting, intends to address this challenge through an efficient and versatile parallelisation framework on top of which two parallel implementations were developed in the present research. The first implementation, the parallelisation of the gradient, shows good scaling behaviour and is sufficiently robust to consistently minimize real large-scale fits. The latter, the parallelisation of the line search, is still work in progress for some specific likelihood components but shows promising results in realistic testcases. Enabling just gradient parallelisation speeds up the full fit of a recently published Higgs combination from the ATLAS experiment by a factor of 4.6 with sixteen workers. As the improvements presented in this research are currently publicly available in ROOT 6.28, we invite users to enable at least gradient parallelisation for robust accelerated fitting with RooFit. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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