hpc2016 のバックアップソース(No.14)

[[MatsuLab. Lecture Note]]
 
*大規模計算論 High Performance Computing [#tee749b0]
:日時|火曜日 １０：４５～１２：１５（３&#12316;４限）~
金曜日 １０：４５～１２：１５（３&#12316;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 |
&color(red,white){メーリングリストに追加しますので、至急TAまでメールを送ってください。Please email to Nagasaka (TA) as soon as possible  in order to add you to the mailing list.};

**目次 [#p6791f79]
#contents

**休講予定日 Lecture Cancelled [#h582bdca]

**授業概要と参考資料 Guidance and References [#af4d5298]
-ガイダンス資料/Guidance &ref("hpc2016_guidance.pdf");
-授業資料/Lecture material https://london.m.gsic.titech.ac.jp/f/5e639824c1/?raw=1

**発表スケジュール Schedule [#p1183393]
&color(red,white){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。};
|CENTER:|CENTER:|CENTER:|CENTER:|LEFT:|c
|回|日付|担当|発表資料|文献|
| 1 | 09/23 | (ガイダンス) |  |  |
| 2 | 09/30 | (Lecture) |  |  |
| 3 | 10/04 | 大山 | &ref("hpc2016_oyama_2.pdf"); | &ref("ipdps16.pdf"); |
| 4 | 10/11 | 小林 | &ref("HPC2016_kobayashi2.pdf"); | &ref("LimitedPrecisionDL.pdf"); |
| 5 | 10/14 | 後藤 | &ref("hpc2016_goto_v2.pdf"); | &ref("AbinitioMDwithML.pdf"); |
| 6 | 10/18 | 津野 | &ref("HPC2016_Tsuno.pdf"); | &ref("Understaing-GPU-Errors.pdf"); |
| 7 | 10/21 | 辻 |  |  |
| 8 | 10/25 | 大沢 |  |  |
| 9 | 10/28 | 伊藤 |  |  |
| 10 | 11/01 | 長沼 |  |  |
| 11 | 11/04 |  |  |  |
| 12 | 11/08 |  |  |  |
| 13 | 11/11 |  |  |  |


** 選択済み論文リスト Selected Papers List [#f836aabb]
- 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)
//**期末レポート Report [#y71cc4e1]
//- &color(red,white){期限 Due date: 02/17 (Extended)};
//- Summarize the general topic covering and including ALL THREE PAPERS regarding the state of the art in HPC and Big Data convergence.
//- It should be 10 pages in [[IEEE conference paper format>http://www.ieee.org/conferences_events/conferences/publishing/templates.html]]
//- Please submit it to TA by email &color(red,white){(NOT mailing list)};

**リンク Links [#s10b4a99]
-[[ACM/IEEE Supercomputing>http://www.supercomp.org]]
-[[IEEE IPDPS>http://www.ipdps.org]]
-[[IEEE HPDC>http://www.hpdc.org/]]
-[[ACM International Conference on Supercomputing (ICS)>http://www.ics-conference.org/]]
-[[ISC>http://www.isc-events.com/]]
-[[IEEE Cluster Computing>http://www.clustercomp.org/]]
-[[IEEE/ACM Grid Computing>http://www.gridcomputing.org/]]
-[[IEEE/ACM CCGrid>http://www.buyya.com/ccgrid/]]
-[[IEEE Big Data>http://cci.drexel.edu/bigdata/bigdata2015/]]
-[[CiteSeer.IST>http://citeseer.ist.psu.edu]]
-[[Google Scholar>http://scholar.google.com]]
-[[Windows Live Academic>http://academic.live.com]]
-[[The ACM Degital Library>http://dl.acm.org/]]