Prediction of the Resource Consumption of Distributed Deep Learning Systems

Gyeongsik Yang, Changyong Shin, Jeunghwan Lee, Yeonho Yoo, Chuck Yoo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Predicting resource consumption for the distributed training of deep learning models is of paramount importance, as it can inform a priori users of how long their training would take and enable users to manage the cost of training. Yet, no such prediction is available for users because the resource consumption itself varies significantly according to "settings"such as GPU types and also by "workloads"like deep learning models. Previous studies have attempted to derive or model such a prediction, but they fall short of accommodating the various combinations of settings and workloads together. This study presents Driple, which designs graph neural networks to predict the resource consumption of diverse workloads. Driple also designs transfer learning to extend the graph neural networks to adapt to differences in settings. The evaluation results show that Driple effectively predicts a wide range of workloads and settings. In addition, Driple can efficiently reduce the time required to tailor the prediction for different settings by up to 7.3×.

Original languageEnglish
Pages (from-to)69-70
Number of pages2
JournalPerformance Evaluation Review
Volume50
Issue number1
DOIs
Publication statusPublished - 2022 Jun

Bibliographical note

Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation grant funded by the Korea government (Ministry of Science and ICT) (2015-0-00280) and by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2021R1A6A1A13044830).

Publisher Copyright:
© 2022 Owner/Author.

Keywords

  • distributed deep learning
  • graph neural networks
  • resource prediction
  • training time prediction
  • transfer learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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