Abstract
Energy efficiency and energy-proportional computing have become a central focus in modern supercomputers. With the exascale computing throughput purported to be bound by the 20 MW power wall, there is an urgent need for strategies that could maximize the performance under a given power budget. In this paper, the quantum chemistry application GAMESS in studied with respect to its behavior under variable power budgets on a dual-socket node. Then, based on the study, a power capping strategy is proposed which dynamically allocates power to various components within a node to maximize performance under a given power budget. Experiments on a 20 core Haswell-EP platform depict that the proposed strategy delivers performance within 2% of the best possible performance for various power budgets in GAMESS.
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