Short description

According to Top500 modern supercomputers are increasingly large (millions of cores), heterogeneous (CPU-GPU, ...), and less reliable (MTBF < 1h) making their programming more complex. The development of parallel algorithms for these ultra-scale supercomputers is in its infancy especially in combinatorial optimization. Our objective is to investigate the MPI+X and PGAS-based approaches for the exascale-aware design and implementation of hybrid algorithms combining exact methods (e.g., B&B) and metaheuristics (e.g., Evolutionary Algorithms) for solving challenging optimization problems. We will address in a holistic (uncommon) way three roadblocks on the road to exascale: locality-aware ultra-scalability, CPU-GPU heterogeneity and checkpointing-based fault tolerance. Our application challenge is to solve to optimality very hard benchmark instances (e.g., Flow-shop ones unsolved for over 30 years). For the validation, various-scale supercomputers will be used, ranging from petascale platforms, to be used for debugging, including Jean Zay (France), UL HPC (Luxembourg), SILECS/Grid’5000 (CPER CornelIA, 2021-2027), and MesoNet (PIA Equipex+, 2021-2027) to exascale supercomputers, to be used for real production, including the first supercomputers of Top500 (e.g., Frontier via our Georgia Tech partner) as well as the two EuroHPC coming ones.