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Exploration of CPU/GPU co-execution: from the perspective of performance, energy, and temperature

Published: 02 November 2011 Publication History

Abstract

In recent computing systems, CPUs have encountered the situations in which they cannot meet the increasing throughput demands. To overcome the limits of CPUs in processing heavy tasks, especially for computer graphics, GPUs have been widely used. Therefore, the performance of up-to-date computing systems can be maximized when the task scheduling between the CPU and the GPU is optimized. In this paper, we analyze the system in the perspective of performance, energy efficiency, and temperature according to the execution methods between the CPU and the GPU. Experimental results show that the GPU leads to better efficiency compared to the CPU when single application is executed. However, when two applications are executed, the GPU does not guarantee superior efficiency than the CPU depending on the application characteristics.

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cover image ACM Conferences
RACS '11: Proceedings of the 2011 ACM Symposium on Research in Applied Computation
November 2011
355 pages
ISBN:9781450310871
DOI:10.1145/2103380
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • SIGAPP: ACM Special Interest Group on Applied Computing
  • ACCT: Association of Convergent Computing Technology
  • CUSST: University of Suwon: Center for U-city Security & Surveillance Technology of the University of Suwon
  • KIISE: Korean Institute of Information Scientists and Engineers
  • KISTI

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2011

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Author Tags

  1. CPU
  2. CUDA
  3. GPU
  4. high-performance computing
  5. scheduling

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RACS '11
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RACS '11: Research in Applied Computation Symposium
November 2 - 5, 2011
Florida, Miami

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