研究概要

主要論文

「*」は、理研外のみでの成果です。

• 1.*Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, and Satoshi Matsuoka.:
  “Large-scale Distributed Second-order Optimization Using Kronecker-factored Approximate Curvature for Deep Convolutional Neural Networks”
  2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), to appear.
• 2.*Yusuke Nagasaka, Akira Nukada, Ryosuke Kojima, and Satoshi Matsuoka.:
  “Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks”
  The 19th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing (CCGrid 2019), to appear.
• 3.*Shweta Salaria, Aleksandr Drozd, Artur Podobas, and Satoshi Matsuoka.:
  "Learning Neural Representations for Predicting GPU Performance"
  ISC High Performance 2019 (ISC'19), to appear.
• 4.*Pak Markthub, and Mehmet E. Belviranli, Seyong Lee, Jeffrey Vetter, and Satoshi Matsuoka.:
  "DRAGON: Breaking GPU Memory Capacity Limits with Direct NVM Access"
  Proceedings of the ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'18), pp 32:1--32:13.
• 5.*Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, and Satoshi Matsuoka.:
  "Accelerating Deep Learning Frameworks with Micro-batches"
  2018 IEEE International Conference on Cluster Computing (CLUSTER 2018) pp 402-412.
• 6.*Yosuke Oyama, Akihiro Nomura, Ikuro Sato, Hiroki Nishimura, Yukimasa Tamatsu, and Satoshi Matsuoka.:
  “Predicting Statistics of Asynchronous SGD Parameters for a Large-Scale Distributed Deep Learning System on GPU Supercomputers”
  2016 IEEE International Conference on Big Data (Big Data 2016), pp. 66-75.

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