[Tccc] [Call-for-Participation] ParLearning 2013, Boston, USA
Yinglong Xia
yinglongxiaatgmail.com
Mon May 20 17:54:03 EDT 2013
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Co-Located with IPDPS 2013, Boston, USA
ParLearning 2013
2nd International Workshop on
Parallel and Distributed Computing for
Machine Learning and Inference Problems
May 24, 2013
http://cass-mt.pnnl.gov/parlearning.aspx
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* KEYNOTES:
- Prof. Srinivasan Parthasarathy, Ohio State University, USA:
Large Scale Data Analytics: Challenges, and the role
of Stratified Data Placement.
- Prof. Srinivas Aluru, Iowa State University, USA
Parallel Methods for Bayesian Network Structure Learning.
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* PROGRAM
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Morning session 1 (08:30-09:40)
8:30-8:40 Opening Remarks
8:40-9:40 Keynote: Prof. Srinivasan Parthasarathy.
Large Scale Data Analytics: Challenges, and the role of
Stratified Data Placement.
9:40-10:00 Coffee break
Morning session 2 (10:00-12:00)
- Combining parallel algorithms solving the same application:
What is the best approach? Alfredo Goldman, Joachim Lepping,
Yanik Ngoko, and Denis Trystram.
- Enhancing Accuracy and Performance of Collaborative Filtering
Algorithm by Stochastic SVD and Its MapReduce Implementation.
Che-Rung Lee and Ya-Fang Chang.
- Reducing False Transactional Conflicts With Speculative
Sub-blocking State - An Empirical Study for ASF Transactional
Memory System. Lifeng Nai and Hsien-Hsin Lee.
- Revisiting a pattern for processing combinatorial objects in
parallel. Christian Trefftz and Jerry Scipps.
Lunch (12:00-13:00)
Afternoon session 1 (13:00-15:00)
13:00-14:00 Keynote: Prof.Srinivas Aluru.
Parallel Methods for Bayesian Network Structure Learning.
- EDA and ML - A Perfect Pair for Large-Scale Data Analysis.
Ryan Hafen and Terence Critchlow.
- Combining Structure and Property Values is Essential for
Graph-based Learning. David J. Haglin and Larry Holder.
- Concluding Remarks
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Topics of Interest
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- Learning and inference using large scale Bayesian Networks
- Scaling up frequent subgraph mining or other graph pattern
mining techniques
- Scalable implementations of learning algorithms for massive
sparse datasets
- Scalable clustering of massive graphs or graph streams
- Scalable algorithms for topic modeling
HPC enabled approaches for emerging trend detection in social
media
- Comparison of various HPC infrastructures for learning
- GPU-accelerated implementations for topic modeling or other
text mining problems
- Knowledge discovery from scientific applications with massive
datasets (climate, systems biology etc.)
- Performance analysis of key machine-learning algorithms from
newer parallel and distributed computing frameworks such as
Mahout, Giraph, IBM Parallel Learning Toolbox, GraphLab etc.
Domain-specific languages for Parallel Computation
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Organizers
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Program Chair:
Chandrika Kamath, Lawrence Livermore National Laboratory, USA
Roger Barga, Microsoft Research, USA
Workshop Co-Chairs:
Yinglong Xia, IBM Research, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
George Chin, Pacific Northwest National Laboratory, USA
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Program Committee
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Anne Hee Hiong Ngu, Texas State University, USA
Anuj Shah, Netflix, USA
Arindam Pal, Indian Institute of Technology, India
Avery Ching, Facebook, USA
Benjamin Herta, IBM Research, USA
Ghaleb Abdulla, Lawrence Livermore National Laboratory, USA
James Montgomery, Australian National University, Australia
Lawrence Holder, Washington State University, USA
Liu Peng, Microsoft, USA
Mahantesh Halappanavar, Pacific Northwest National Laboratory, USA
Mladen Vouk, North Carolina State University, USA
Oreste Villa, Pacific Northwest National Laboratory, USA
Simon Kahan, University of Washington, USA
Yangqiu Song, Microsoft Research, China
Yaohang Li, Old Dominion University, USA
Yihua Huang, Nanjing University, USA
Yi Wang, Tencent Holdings, China
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