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Auto-tuning methodology for configuration and application parameters of hybrid CPU + GPU parallel systems based on expert knowledge

Abstract

Auto-tuning of configuration and application param- eters allows to achieve significant performance gains in many contemporary compute-intensive applications. Feasible search spaces of parameters tend to become too big to allow for exhaustive search in the auto-tuning process. Expert knowledge about the utilized computing systems becomes useful to prune the search space and new methodologies are needed in the face of emerging heterogeneous computing architectures. In this paper we propose an auto-tuning methodology for hybrid CPU/GPU applications that takes into account previous execution experi- ences, along with an automated tool for iterative testing of chosen combinations of configuration, as well as application-related parameters. Experimental results, based on a parallel similarity search application executed on three different CPU+GPU parallel systems, show that the proposed methodology allows to achieve execution times worse by only up to 8% compared to a search algorithm that performs a full search over combinations of application parameters, while taking only up to 26% time of the latter.

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Copyright (2019 IEEE)

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Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Title of issue:
2019 International Conference on High Performance Computing & Simulation (HPCS) strony 551 - 558
Language:
English
Publication year:
2020
Bibliographic description:
Czarnul P., Rościszewski P.: Auto-tuning methodology for configuration and application parameters of hybrid CPU + GPU parallel systems based on expert knowledge// 2019 International Conference on High Performance Computing & Simulation (HPCS)/ : , 2020, s.551-558
DOI:
Digital Object Identifier (open in new tab) 10.1109/hpcs48598.2019.9188060
Sources of funding:
  • Statutory activity/subsidy
Verified by:
Gdańsk University of Technology

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