Designing and operating distributed data infrastructures and computing centres poses challenges in areas such as networking, architecture, storage, databases, and cloud. These challenges are amplified and added to when operating at the extremely large scales required by major scientific endeavours. CERN is evaluating different models for increasing computing and data-storage capacity, in order to accommodate the growing needs of the LHC experiments over the next decade. All models present different technological challenges. In addition to increasing the on-premises capacity of the systems used for traditional types of data processing and storage, explorations are being carried out into a number of complementary distributed architectures and specialised capabilities offered by cloud and HPC infrastructures. These will add heterogeneity and flexibility to the data centres, and should enable advances in resource optimisation.

The next-generation of HPC technology offers great promise for supporting scientific research. Exascale supercomputers – machines capable of performing a quintillion, or a billion billion, calculations per second – are now becoming a reality. This change in the power of HPC technology, coupled with growing use of machine learning, will be vital in ensuring the success of future big science projects, such as the High-Luminosity Large Hadron Collider (HL-LHC).

CERN is working with other leading organisations to ensure readiness and realise the full potential of the coming new generation of HPC technology. For example, in 2020 CERN was one of four leading organisations to form a pioneering collaboration to work on overcoming challenges related to the use of HPC to support large, data-intensive science projects. The other members of the collaboration are SKAO, the organisation leading the development of the Square Kilometre Array radio-telescope; GÉANT, the pan-European network and services provider for research and education; and PRACE, the Partnership for Advanced Computing in Europe.