Technology

Members of the EU project “CoE RAISE” – development of artificial intelligence for next-generation supercomputers – meet at CERN

Last week, members of the EU’s CoE RAISE project met at CERN for their “All Hands” meeting. This innovative project develops artificial intelligence (AI) approaches for next-generation “exascale” supercomputers for use across both science and industry. Use cases explored through the project include optimization of wind farm layouts, design of efficient aircraft, improved sound engineering, seismic imaging with remote sensing and more.

CoE RAISE – European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” – is funded under the EU’s Horizon 2020 research and innovation programme. The project started in 2021 and will run for three years.

Fifty-four project members participated in the four-day meeting, which took place in CERN’s Council Chamber. Participants discussed progress in their work to develop AI technologies for complex applications in Europe running on future “exascale” high-performance computing (HPC) systems. Exascale refers to the next generation of high-performance computers that can perform over 1018 floating-point operations per second (FLOPS). Only today The Frontier supercomputer at Oak Ridge National Laboratory in the US has reached this level. However, with more exascale HPC systems just over the horizon, it is important to ensure that AI approaches used in science and industry are ready to fully exploit the enormous potential. In June, the European High Performance Computing Joint Undertaking (EuroHPC JU) announced that Forschungszentrum Jülich GmbH in Germany has been selected to host and operate Europe’s first exascale supercomputerwhich is set to come online next year and will be known as JUPITER (Joint Undertaking Pioneer for Innovative and Transformative Exascale Research).

CoE RAISE develops innovative AI methods on heterogeneous HPC architectures involving multiple kinds of processors. Such architectures can offer higher performance and energy efficiency, but code must be adapted to use the different types of processors efficiently. The AI ​​methods being developed are focused around nine important use cases and designed to scale well for running on exascale HPC systems.

CoE RAISE supports technology transfer to industry, especially small and medium-sized enterprises, as well as the implementation of education and training initiatives. In addition to this, CoE RAISE also provides advice and contacts with other European initiatives to maximize synergies, exploit opportunities for co-design and share knowledge. All aspects of the project’s work were discussed during the four days at CERN.

CERN is also a partner and brings one of the use cases to the project. This work focuses on improving methods for reconstructing particle collision events at the upgraded High-Luminosity Large Hadron Collider (HL-LHC), which is set to come online in 2029. The HL-LHC will see more particle collisions than ever before. place that produces exabytes of data every year, resulting in unprecedented computing challenges. To reconstruct particle collision events today (with datasets on the order of terabytes or petabytes), hundreds of different algorithms run simultaneously: some are traditional algorithms optimized for specific hardware configurations, while others already include AI-powered methods, such as deep neural networks (DNNs). The members of the project team at CERN are working to increase the modularity of the systems and ensure that code is optimized to take full advantage of heterogeneous architectures, as well as increase the use of machine learning and other AI methods for collision reconstruction and particle classification.

“Supercomputers reach the exascale and enable the delivery of an unprecedented scale of processing resources for HPC and AI workflows,” says Maria Girone, CERN’s openlab CTO, who leads CERN’s contribution to the project. “Research conducted in CoE RAISE will drive the co-design of HPC computing resources for future AI and HPC applications for both science and industry. This meeting enabled us to exchange and develop ideas and bring new perspectives. It also provided researchers from other domains a unique insight into the environment and challenges facing CERN, promoting cross-fertilization and understanding.”

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