Domain knowledge specification for energy tuning. (6th July 2018)

• Record Type: Journal Article
• Authors: Kumaraswamy, Madhura; Chowdhury, Anamika; Gerndt, Michael; Bendifallah, Zakaria; Bouizi, Othman; Locans, Uldis; Říha, Lubomír; Vysocký, Ondřej; Beseda, Martin; Zapletal, Jan
• Other Names: Ezzatti Pablo guestEditor.; Quintana‐Ortí Enrique S. guestEditor.; Remón Alfredo guestEditor.; Saak Jens guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 6(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-06
• Subjects: automatic tuning -- domain knowledge -- DVFS -- energy efficiency -- high performance computing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4650
• Languages: English
• ISSNs: 1532-0626

Abstract

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.