MatsuLab. Lecture Note
大規模計算論 High Performance Computing †
- 日時
- 火曜日 １０：４５〜１２：１５（３〜４限）
金曜日 １０：４５〜１２：１５（３〜４限）
- 場所
- 本館 １１４号室（H１１４）
- 連絡
| 松岡教授 (Prof. S.Matsuoka) | matsu [at] is. |
| TA 長坂 (Y.Nagasaka) | nagasaka.y.aa [at] m.titech.ac.jp |
目次 †
休講予定日 Lecture Cancelled †
11/1 (Tue)
授業概要と参考資料 Guidance and References †
発表スケジュール Schedule †
暫定的な割り当ては以下の通りですが、都合が悪い場合はTAまで希望日をメールしてください。
選択済み論文リスト Selected Papers List †
- S. Sallinen, N. Satish, M. Smelyanskiy, S. Sury, C. Re. High Performance Parallel Stochastic Gradient Descent in Shared Memory. IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2016.
- H. Cui, H. Zhang, G. R. Ganger, P. B. Gibbons, and E. P. Xing. Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server. In Proceedings of the Eleventh European Conference on Computer Systems, EuroSys? ’16, pages 4:1?4:16, New York, NY, USA, 2016. ACM.
- J. K. Kim, Q. Ho, S. Lee, X. Zheng, W. Dai, G. A. Gibson, and E. P. Xing. Strads: A distributed framework for scheduled model parallel machine learning. In Proceedings of the Eleventh European Conference on Computer Systems, EuroSys? ’16, pages 5:1?5:16, New York, NY, USA, 2016. ACM.
- Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan and Pritish Narayanan: Deep Learning with Limited Numerical Precision. Proceedings of the 32nd International Conference on Machine Learning (ICML-15)
- Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman, Microsoft Research: Project Adam: Building an Efficient and Scalable Deep Learning Training System. the Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI14)
- Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung: ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines. Parallel and Distributed Processing Symposium, 2016 IEEE International (IPDPS 2016)
リンク Links †
![[PukiWiki]](image/pukiwiki.png "[PukiWiki]")
hpc2016_guidance.pdf
