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• W2800144703 abstract "Summary In this work, we propose a method that allows us to reduce energy consumption of an application executed on supercomputing centers. The proposed method is based on a mixed precision arithmetic where the precision of data is calibrated at runtime. For this reason, we develop a modified version of the random forest algorithm. The effectiveness of the proposed approach is validated with a real‐life scientific application called MPDATA, which is part of the numerical model used in weather forecasting. The energy efficiency of the proposed method is examined using two GPU‐based clusters. The first of them is the Piz Daint supercomputer, currently ranked 3rd at the TOP500 list (November 2017). It is equipped with NVIDIA Tesla P100 GPU accelerators based on the Pascal architecture. The second is the MICLAB cluster containing NVIDIA Tesla K80 based on the Kepler architecture. The achieved results show that the proposed machine learning method allows us to provide the accuracy of computation comparable with that achieved double precision and reduce the energy consumption up to 36% compared to the double precision version of MPDATA." @default.
• W2800144703 created "2018-05-17" @default.
• W2800144703 creator A5041427052 @default.
• W2800144703 date "2018-04-30" @default.
• W2800144703 modified "2023-09-27" @default.
• W2800144703 title "Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU-based supercomputers" @default.
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• W2800144703 doi "https://doi.org/10.1002/cpe.4644" @default.
• W2800144703 hasPublicationYear "2018" @default.
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