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In line with various domains, the advent of Big Data presents challenges in the Large Hadron Collider (LHC) computing. Harnessing the increasing amount of intricate data stresses the current computing resources and significant Research and Development (R&D) efforts are needed to maintain within budgetary constraints at the High-Luminosity LHC era. In parallel, the emerging computing hardware necessitates a re-engineering of the scientific software.This seminar focuses on computing challenges encountered in the detector simulation and data analytics domains, providing insights through the lens of the ATLAS experiment and showcasing R&D projects addressing them. Initially, an overview of LHC computing will be presented, alongside international initiatives facilitating cooperation and common efforts in the broader HEP computing realm. Challenges in the detector simulation domain are typically tackled by either optimizing and porting to acceleration hardware the Geant4 simulation toolkit, or by employing fast simulators based on parametrized models and generative machine learning algorithms. Addressing challenges in the data analytics domain involves devising new data processing models based on compact data formats and an array-programming paradigm, which efficiently processes multiple events simultaneously. Finally, considerations regarding the workforce of research software developers will be discussed.
Videoconference via https://us02web.zoom.us/j/88356222832