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.

Following a pioneering workshop on quantum computing held at CERN in 2018, CERN openlab started a number of projects in quantum computing that are at different stages of realisation. These projects feed into the CERN Quantum Technology Initiative, launched in 2020.

While quantum computing should not be considered a panacea, this technology does hold significant potential:

• It is poised to unlock unprecedented levels of computing power, improving our collective ability to simulate complex systems and understand the world around us.
• It is likely to be much greener than classical forms of computing.
• Quantum communication and encryption techniques are also exciting; these could, for example, play an important role in protecting citizens’ privacy.

CERN is exceptionally well placed for quantum computing: as well as having experience with complex algorithms and with the physics that underpin this technology, the Organization has the required expertise in cryogenics, electronics, and materials. Nevertheless, we know that quantum computing technologies can only achieve their full potential for good if access is equitable and appropriate governance agreements and security systems are put in place. While the technology itself may be a decade or more away, the work to achieve these things needs to start now. As with AI technologies today, and the Web and the Grid earlier, CERN will be working with stakeholders across society — in an open, transparent manner — to achieve this, with a goal of improving the lives of people across the globe. Find out more on quantum.cern.

By working with communities beyond high-energy physics, we are able to ensure maximum relevancy for CERN openlab’s work, as well as learning and sharing both tools and best practices across scientific fields. Today, more and more research fields, such as medical research or space and Earth observation research, are driven by large quantities of data, and thus experience computing challenges comparable to those at CERN.

CERN openlab’s mission rests on three pillars: technological investigation, education, and dissemination. Collaborating with research communities and laboratories outside the high-energy physics community brings together all these aspects. Challenges related to the life sciences, medicine, astrophysics, and urban/environmental planning are all covered in this section, as well as scientific platforms designed to foster open collaboration.

The High-Luminosity LHC (HL-LHC), set to come online in 2029, will require roughly ten times the computing capacity we have today at CERN. Data-storage needs will also outstrip what it is possible to achieve with a constant investment budget by several factors. Even taking into account the expected evolution of technology, there will be a substantial shortage of IT resources. Thus, CERN openlab is exploring new and innovative solutions to help physicists bridge this resource gap, which may otherwise impact on the HL-LHC experimental programme.

Members of CERN’s research community expend significant efforts to understand how they can get the most value out of the data produced by the LHC experiments. They seek to maximise the potential for discovery and employ new techniques to help ensure that nothing is missed. At the same time, it is important to optimise resource usage (tape, disk, and CPU), both in the online and offline environments.

Modern machine-learning technologies — in particular, deep-learning solutions — offer a promising research path to achieving these goals. Deep-learning techniques offer the LHC experiments the potential to improve performance in each of the following areas: particle detection, identification of interesting events, modelling detector response in simulations, monitoring experimental apparatus during data taking, and managing computing resources.