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Publication List - Rio Yokota 2018 (13 / 183 entries)
Journal Paper
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Naoya Maruyama,
Takayuki Aoki,
Kenjiro Taura,
Rio Yokota,
Mohamed Wahib,
Motohiko Matsuda,
Keisuke Fukuda,
Takashi Shimokawabe,
Naoyuki Onodera,
Michel Müller,
Shintaro Iwasaki.
Highly Productive, High-Performance Application Frameworks for Post-Petascale Computing,
Advanced Software Technologies for Post-Peta Scale Computing,
pp. 77--98,
Dec. 2018.
International Conference (Reviewed)
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Ichitaro Yamazaki,
Ahmad Abdelfattah,
Akihiro Ida,
Satoshi Ohshima,
Stanimire Tomov,
Rio Yokota,
Jack Dongarra.
Analyzing Performance of BiCGStab with Hierarchical Matrix on GPU clusters,
32nd IEEE International Parallel & Distributed Processing Symposium,
May 2018.
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Satoshi Ohshima,
Ichitaro Yamazaki,
Akihiro Ida,
Rio Yokota.
Optimization of Hierarchical Matrix Computation on GPU,
SC Asia,
Mar. 2018.
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Hiroki Naganuma,
Rio Yokota.
Accelerating Convolutional Neural Networks Using Low Precision Arithmetic,
HPC Asia,
Jan. 2018.
Domestic Conference (Reviewed)
International Conference (Not reviewed / Unknown)
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Rio Yokota.
Optimization Methods for Large Scale Distributed Deep Learning,
IPAM Workshop I: Big Data Meets Large-Scale Computing,
Sept. 2018.
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Rio Yokota.
Early Application Results on TSUBAME 3,
Smoky Mountains Computational Sciences and Engineering Conference,
Aug. 2018.
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Rio Yokota.
Scaling Deep Learning to Thousands of GPUs,
HPC 2018,
July 2018.
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Rio Yokota.
Energy Conserving Fast Multipole Methods for the Calculation of Long-range Interactions,
Mathematics in Action: Modeling and analysis in molecular biology and electro- physiology,
June 2018.
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Rio Yokota.
Can we use Hierarchical Low-Rank Approximation for Deep Learning?,
HPC Saudi 2018,
Mar. 2018.
Domestic Conference (Not reviewed / Unknown)
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Yuji Kuwamura,
Kazuki Osawa,
Rio Yokota.
Hyper-parameter Tuning for Approximate Natural Gradient Methods,
The 80th National Convention of IPSJ,
Mar. 2018.
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Hiroyuki Otomo,
Kazuki Osawa,
Rio Yokota.
Distributed Learning of Deep Neural Networks Using the Kronecker Factorization of the Fisher Information Matrix,
The 163rd Workshop on High Performance Computing,
Mar. 2018.
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Hiroyuki Ohtomo,
Kazuki Osawa,
Rio yokota.
Deep Learning Using Kronecker-factored Approximation of Fisher Matrix,
The 80th National Convention of IPSJ,
Mar. 2018.
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