Resource-aware device allocation of data-parallel applications on heterogeneous systems

Donghyeon Kim, Seokwon Kang, Junsu Lim, Sunwook Jung, Woosung Kim, Yongjun Park

Research output: Contribution to journalArticlepeer-review

Abstract

As recent heterogeneous systems comprise multi-core CPUs and multiple GPUs, efficient allocation of multiple data-parallel applications has become a primary goal to achieve both maximum total performance and efficiency. However, the efficient orchestration of multiple applications is highly challenging because a detailed runtime status such as expected remaining time and available memory size of each computing device is hidden. To solve these problems, we propose a dynamic data-parallel application allocation framework called ADAMS. Evaluations show that our framework improves the average total execution device time by 1.85× over the round-robin policy in the non-shared-memory system with small data set.

Original languageEnglish
Article number1825
Pages (from-to)1-18
Number of pages18
JournalElectronics (Switzerland)
Volume9
Issue number11
DOIs
Publication statusPublished - 2020 Nov

Bibliographical note

Funding Information:
Funding: This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: 2018R1D1A1B07050609), by Institute for Information & communication Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00142), and by the LG Electronics CTO Software Platform Lab. grant funded by the LG Electronics Inc.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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