1. Reliable power and time-constraints-aware predictive management of heterogeneous exascale systems
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William Fornaciari, Carlo Brandolese, Alessandro Cilardo, Carles Hernandez, José María Torralba Martínez, Luca Cremona, Michal Kulchewski, Simone Libutti, Giovanni Agosta, Jose Flich, Ariel Oleksiak, Marina Zapater, Giuseppe Massari, Albert Farrés, Rafael Tornero, David Atienza, Michele Zanella, Federico Reghenzani, Anna Pupykina, Leila Cammoun, and Davide Zoni
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Exploit ,Computer science ,Distributed computing ,Cloud computing ,02 engineering and technology ,Multi many cores ,run time management ,models ,0202 electrical engineering, electronic engineering, information engineering ,power optimizaton ,Massively parallel ,020203 distributed computing ,business.industry ,mpi/openmp ,simulation ,Exascale computing ,020202 computer hardware & architecture ,Power (physics) ,Range (mathematics) ,multi ,HPC ,Central processing unit ,business ,Efficient energy use ,Multi many cores, run time management, power optimizaton, HPC ,energy - Abstract
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].
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