GSS EU Science Funding Gateway

Proposals should provide a holistic view on the entire energy aware software stack, including a dynamic resource and workload management solution for energy aware HPC, a comprehensive monitoring and profiling framework adhering to a common data format. Participants must commit to work towards a common standard and solution to address the challenges of energy efficiency, monitoring and resource scheduling at exascale. All participating HPC centres should gradually adopt the common solution once the required quality level has been reached which should be measures by appropriate KPIs. The developed solution should integrate an advanced data analytics solution providing the basis for automated intelligent decisions on scheduling, dynamic resource allocation, job and application configuration. The data driven solution should provide capabilities significantly beyond the state of the art, considering recent developments in the fields of modelling and optimisation, data science, deep learning and generative AI. Moreover, the solution should identify user input and applications resulting in inefficient use of resources to provide automated feedback on the optimal use of resources.

Proposals should indicate, for each participating HPC centre, the current energy consumption with respect to the current throughput and portfolio of applications (on average, expressed in Gflops/Watt) and an estimated overall reduction of energy consumption by the end of the action when the software stack is expected to be used in production.