https://doi.org/10.1140/epjp/s13360-023-03861-1
Regular Article
HEP-Frame: an efficient tool for big data applications at the LHC
1
High-Assurance Software Laboratory, INESC TEC, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
2
Department Physics, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
3
Algoritmi Center, Department Informatics, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
a
andre.martins.pereira@inesctec.pt
Received:
19
September
2022
Accepted:
5
March
2023
Published online:
25
March
2023
HEP-Frame is a new C++ package designed to efficiently perform analyses of datasets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high-performance servers and mini-clusters, and it was designed for natural science researchers with a user-friendly interface to access structured databases. HEP-Frame automatically evaluates the underlying computing resources and builds an adequate code skeleton when creating a data analysis application. At run-time, HEP-Frame analyses a sequence of datasets exploring the available parallelism in the code and hardware resources: it concurrently reads inputs from a user-defined data structure and processes them, following the user-specific sequence of requirements to select relevant data; it manages the efficient execution of that sequence; and it outputs results in user-defined objects (e.g., ROOT structures), stored together with the used input dataset. This paper shows how a domain expert software development can benefit from HEP-Frame, and how it significantly improved the performance of analyses of large datasets produced in proton-proton collisions at the LHC. Two case studies are discussed: the associated production of top quarks together with a Higgs boson (
) at the LHC, and a double- and single-top quark productions at the high-luminosity phase of the LHC (HL-LHC). Results show that the HEP-Frame awareness of the analysis code behaviour and structure, and the underlying hardware system, provides powerful and transparent parallelization mechanisms that largely improve the execution time of data analysis applications.
© The Author(s) 2023
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