Distributed Parallel Deep Learning for Fast Extraction of Similar Weather Map

Boseon Kang, Jae Heon Jeong, Changsung Jeong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


For real time weather forecasting, it is necessary to search most similar weather map very fast among a large amount of data accumulated so far. Recently, deep learning is used for more accurate weather forecasting. However, it takes a huge amount of time for training deep learning model in order to process a number of previous weather maps. In this paper, we shall present fast distributed parallel algorithms for training deep neural network model based on CNN on parallel and distributed environment with GPUs for various number of models in order to extract most similar weather map from CNN. For each case of single and multi nodes, we compare the performance of our algorithm increasing the number of GPUs, and for the case of multi nodes, compare the performance for two ways of communications: synchronous and asynchronous. Also, we shall show the performance of our algorithm for the various number of models on single and multi nodes.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781538654576
Publication statusPublished - 2018 Jul 2
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 2018 Oct 282018 Oct 31

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03035461), the Brain Korea 21 Plus Project in 2018, and the Institute for Information & communications Technology Promotion(IITP) grant funded by the Korean government (MSIP) (No. 2018-0-00739, Deep learning-based natural language contents evaluation technology for detecting fake news) and the utilization of satellite information project through the Korea Aerospace Research Institute (KARI).

Publisher Copyright:
© 2018 IEEE.


  • Deep learning
  • Distributed Environment
  • Similar weather map
  • Weather Prediction

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Distributed Parallel Deep Learning for Fast Extraction of Similar Weather Map'. Together they form a unique fingerprint.

Cite this