Soft Memory Box: A Virtual Shared Memory Framework for Fast Deep Neural Network Training in Distributed High Performance Computing

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dc.contributor.authorAhn, Shinyoungko
dc.contributor.authorKim, Joongheonko
dc.contributor.authorLim, Eunjiko
dc.contributor.authorKang, Sungwonko
dc.date.accessioned2018-07-24T02:22:27Z-
dc.date.available2018-07-24T02:22:27Z-
dc.date.created2018-07-04-
dc.date.created2018-07-04-
dc.date.issued2018-06-
dc.identifier.citationIEEE ACCESS, v.6, pp.26493 - 26504-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10203/244019-
dc.description.abstractDeep learning is one of the major promising machine learning methodologies. Deep learning is widely used in various application domains, e.g., image recognition, voice recognition, and natural language processing. In order to improve learning accuracy, deep neural networks have evolved by: 1) increasing the number of layers and 2) increasing the number of parameters in massive models. This implies that distributed deep learning platforms need to evolve to: 1) deal with huge/complex deep neural networks and 2) process with high-performance computing resources for massive training data. This paper proposes a new virtual shared memory framework, called Soft Memory Box (SMB), which enables sharing the memory of remote node among distributed processes in the nodes so as to improve communication performance via parameter sharing. According to data-intensive performance evaluation results, the communication time of deep learning using the proposed SMB is 2.1 times faster than that using the massage passing interface (MPI). In addition, the communication time of the SMB-based asynchronous parameter update becomes 2-7 times faster than that using the MPI depending on deep learning models and the number of deep learning workers.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleSoft Memory Box: A Virtual Shared Memory Framework for Fast Deep Neural Network Training in Distributed High Performance Computing-
dc.typeArticle-
dc.identifier.wosid000434945000001-
dc.identifier.scopusid2-s2.0-85046765445-
dc.type.rimsART-
dc.citation.volume6-
dc.citation.beginningpage26493-
dc.citation.endingpage26504-
dc.citation.publicationnameIEEE ACCESS-
dc.identifier.doi10.1109/ACCESS.2018.2834146-
dc.contributor.localauthorKang, Sungwon-
dc.contributor.nonIdAuthorKim, Joongheon-
dc.contributor.nonIdAuthorLim, Eunji-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
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