Reliable power and time-constraints-aware predictive management of heterogeneous exascale systems
William Fornaciari, Giovanni Agosta, David Atienza, Carlo Brandolese, Leila Cammoun, Luca Cremona, Alessandro Cilardo, Albert Farrés, José Flich, Carles Hernández, Michal Kulchewski, Simone Libutti, José Maria Martínez, Giuseppe Massari, Ariel Oleksiak, Anna Pupykina, Federico Reghenzani, Rafael Tornero, Michele Zanella, Marina Zapater · 2018
The transition to Exascale computing is going to be characterised by an increased range of application classes. In addition to traditional massively parallel "number crunching" applications, new classes are emerging such as real-time HPC and data-intensive scalable computing. Furthermore, Exascale computing is characterised by a "democratisation" of HPC: to fully exploit the capabilities of Exascale-level facilities, HPC is moving towards enabling access to its resources to a wider range of new players, including SMEs, through cloud-based approaches [1]. Finally, the need for much higher energy efficiency is pushing towards deep heterogeneity, widening the range of options for acceleration, moving from the traditional CPU-only organization, to the CPU plus GPU which currently dominates the Green5001, to more complex options including programmable accelerators and even (reconfigurable) hardware accelerators [2].