Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.

The HEPiX Benchmarking Working Group has developed a framework to benchmark the performance of a computational server using the software applications of the High Energy Physics (HEP) community. This framework consists of two main components, named HEP-Workloads and HEPscore. HEP-Workloads is a collection of standalone production applications provided by a number of HEP experiments. HEPscore is designed to run HEP-Workloads and provide an overall measurement that is representative of the computing power of a system. HEPscore is able to measure the performance of systems with different processor architectures and accelerators. The framework is completed by the HEP Benchmark Suite that simplifies the process of executing HEPscore and other benchmarks such as HEP-SPEC06, SPEC CPU 2017, and DB12. This paper describes the motivation, the design choices, and the results achieved by the HEPiX Benchmarking Working group. A perspective on future plans is also presented.

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The Large Hadron Collider (LHC) will enter a new phase beginning in 2027 with the upgrade to the High Luminosity LHC (HL-LHC). The increase in the number of simultaneous collisions coupled with a more complex structure of a single event will result in each LHC experiment collecting, storing, and processing exabytes of data per year. The amount of generated and/or collected data greatly outweighs the expected available computing resources. In this paper, we discuss effcient usage of HPC resources as a prerequisite for data-intensive science at exascale. First, we discuss the experience of porting CMS Hadron and Electromagnetic calorimeters reconstruction code to utilize Nvidia GPUs within the DEEP-EST project; second, we look at the tools and their adoption in order to perform benchmarking of a variety of resources available at HPC centers. Finally, we touch on one of the most important aspects of the future of HEP - how to handle the flow of petabytes of data to and from computing facilities, be it clouds or HPCs, for exascale data processing in a flexible, scalable and performant manner. These investigations are a key contribution to technical work within the HPC collaboration among CERN, SKA, GEANT and PRACE.