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Domain knowledge specification for energy tuning
Author(s) -
Kumaraswamy Madhura,
Chowdhury Anamika,
Gerndt Michael,
Bendifallah Zakaria,
Bouizi Othman,
Locans Uldis,
Říha Lubomír,
Vysocký Ondřej,
Beseda Martin,
Zapletal Jan
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4650
Subject(s) - computer science , dynamism , suite , domain (mathematical analysis) , efficient energy use , energy (signal processing) , energy consumption , performance tuning , distributed computing , domain knowledge , computer engineering , computer architecture , software engineering , operating system , engineering , mathematical analysis , statistics , physics , mathematics , archaeology , quantum mechanics , electrical engineering , history
Summary To overcome the challenges of energy consumption of HPC systems, the European Union Horizon 2020 READEX (Runtime Exploitation of Application Dynamism for Energy‐efficient Exascale computing) project uses an online auto‐tuning approach to improve energy efficiency of HPC applications. The READEX methodology pre‐computes optimal system configurations at design‐time, such as the CPU frequency, for instances of program regions and switches at runtime to the configuration given in the tuning model when the region is executed. READEX goes beyond previous approaches by exploiting dynamic changes of a region's characteristics by leveraging region and characteristic specific system configurations. While the tool suite supports an automatic approach, specifying domain knowledge such as the structure and characteristics of the application and application tuning parameters can significantly help to create a more refined tuning model. This paper presents the means available for an application expert to provide domain knowledge and presents tuning results for some benchmarks.