文献
J-GLOBAL ID:201902267106170765
整理番号:19A0498701
深い学習タスクにおける成形可能で不正なスケジューリングの有効性【JST・京大機械翻訳】
Effectiveness of Moldable and Malleable Scheduling in Deep Learning Tasks
著者 (4件):
Fujiwara Ikki
(Data-driven Intelligent System Research Center (DIRECT) Universal Communication Research Institute, National Institute of Information and Communications Technology (NICT), 3-5 Hikaridai, Seika-cho, Kyoto, 619-0289, Japan)
,
Tanaka Masahiro
(Data-driven Intelligent System Research Center (DIRECT) Universal Communication Research Institute, National Institute of Information and Communications Technology (NICT), 3-5 Hikaridai, Seika-cho, Kyoto, 619-0289, Japan)
,
Taura Keniiro
(Graduate School of Information Science and Technology Department of Information and Communication Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan)
,
Torisawa Kentaro
(Data-driven Intelligent System Research Center (DIRECT) Universal Communication Research Institute, National Institute of Information and Communications Technology (NICT), 3-5 Hikaridai, Seika-cho, Kyoto, 619-0289, Japan)
資料名:
IEEE Conference Proceedings
(IEEE Conference Proceedings)
巻:
2018
号:
ICPADS
ページ:
389-398
発行年:
2018年
JST資料番号:
W2441A
資料種別:
会議録 (C)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)