Supercomputers have been extensively used to solve complex scientific and engineering problems, boosting the capability to design more efficient systems. The pace at which data are generated by scientific experiments and large simulations (e.g., multiphysics, climate, weather forecast, etc.) poses new challenges in terms of capability of efficiently and effectively analysing massive data sets. Artificial Intelligence (AI), and more specifically Machine Learning (ML) and Deep Learning (DL) recently gained momentum for boosting simulations’ speed. ML/DL techniques are part of simulation processes, used to early detect patterns of interests from less accurate simulation results.
To address these challenges, the ACROSS project will codesign and develop an HPC, Big Data, AI convergent platform, supporting applications in the aeronautics, climate and weather, and energy domains. To this end, ACROSS will leverage on next generation of pre-exascale infrastructures, still being ready for exascale systems, and on effective mechanisms to easily describe and manage complex workflows in these three domains. Energy efficiency will be achieved by massive use of specialized hardware accelerators, monitoring running systems and applying smart mechanisms of scheduling jobs.
ACROSS will combine traditional HPC techniques with AI (specifically ML/DL) and Big Data analytic techniques to enhance the application test case outcomes (e.g., improve the existing operational system for global numerical weather prediction, climate simulations, develop an environment for user-defined in-situ data processing, improve and innovate the existing turbine aero design system, speed up the design process, etc.). The performance of ML/DL will be accelerated by using dedicated hardware devices.
ACROSS will promote cooperation with other EU initiatives (e.g. EPI) and future EuroHPC projects to foster the adoption of exascale-level computing among test case domain stakeholders