ParLearning 2014 - ParLearning 2014 : Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics

ParLearning 2014
The 3rd International Workshop on
Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics
May 23, 2014
Phoenix, AZ, USA
In Conjunction with IPDPS 2014
Data-driven computing needs no introduction today. The case for using data for strategic advantages is exemplified by web search engines, online translation tools and many more examples. The past decade has seen 1) the emergence of multicore architectures and accelerators as GPGPUs, 2) widespread adoption of distributed computing via the map-reduce/hadoop eco-system and 3) democratization of the infrastructure for processing massive datasets ranging into petabytes by cloud computing. The complexity of the technological stack has grown to an extent where it is imperative to provide frameworks to abstract away the system architecture and orchestration of components for massive-scale processing. However, the growth in volume and heterogeneity in data seems to outpace the growth in computing power. A “collect everything” culture stimulated by cheap storage and ubiquitous sensing capabilities contribute to increasing the noise-to-signal ratio in all collected data. Thus, as soon as the data hits the processing infrastructure, determining the value of information, finding its rightful place in a knowledge representation and determining subsequent actions are of paramount importance. To use this data deluge to our advantage, a convergence between the field of Parallel and Distributed Computing and the interdisciplinary science of Artificial Intelligence seems critical. From application domains of national importance as cyber-security, health-care or smart-grid to providing real-time situational awareness via natural interface based smartphones, the fundamental AI tasks of Learning and Inference need to be enabled for large-scale computing across this broad spectrum of application domains.
Many of the prominent algorithms for learning and inference are notorious for their complexity. Adopting parallel and distributed computing appears as an obvious path forward, but the mileage varies depending on how amenable the algorithms are to parallel processing and secondly, the availability of rapid prototyping capabilities with low cost of entry. The first issue represents a wider gap as we continue to think in a sequential paradigm. The second issue is increasingly recognized at the level of programming models, and building robust libraries for various machine-learning and inferencing tasks will be a natural progression. As an example, scalable versions of many prominent graph algorithms written for distributed shared memory architectures or clusters look distinctly different from the textbook versions that generations of programmers have grown with. This reformulation is difficult to accomplish for an interdisciplinary field like Artificial Intelligence for the sheer breadth of the knowledge spectrum involved. The primary motivation of the proposed workshop is to invite leading minds from AI and Parallel & Distributed Computing communities for identifying research areas that require most convergence and assess their impact on the broader technical landscape.
HIGHLIGHTS
Foster collaboration between HPC community and AI community
Applying HPC techniques for learning problems
Identifying HPC challenges from learning and inference
Explore a critical emerging area with strong academia and industry interest
Great opportunity for researchers worldwide for collaborating with Academia and Industry
CALL FOR PAPERS
Authors are invited to submit manuscripts of original unpublished research that demonstrate a strong interplay between parallel/distributed computing techniques and learning/inference applications, such as algorithm design and libraries/framework development on multicore/ manycore architectures, GPUs, clusters, supercomputers, cloud computing platforms that target applications including but not limited to:
Learning and inference using large scale Bayesian Networks
Large scale inference algorithms using parallel TPIC models, clustering and SVM etc.
Parallel natural language processing (NLP).
Semantic inference for disambiguation of content on web or social media
Discovering and searching for patterns in audio or video content
On-line analytics for streaming text and multimedia content
Comparison of various HPC infrastructures for learning
Large scale learning applications in search engine and social networks
Distributed machine learning tools (e.g., Mahout and IBM parallel tool)
Real-time solutions for learning algorithms on parallel platforms
More detail. PDF version
IMPORTANT DATE
Workshop Paper Due
December 30, 2013
Author Notification
February 14, 2014
Camera-ready Paper Due
March 14, 2014
PAPER GUIDELINES
Submitted manuscripts may not exceed 10 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references. More format requirements will be posted on the IPDPS web page (www.ipdps.org) shortly after the author notification Authors can purchase up to 2 additional pages for camera-ready papers after acceptance. Please find details on www.ipdps.org. Students with accepted papers have a chance to apply for a travel award. Please find details at www.ipdps.org.
Submit your paper using EDAS portal for ParLearning: http://edas.info/N15817
PROCEEDINGS
All papers accepted by the workshop will be included in the proceedings of the IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (IPDPSW), indexed in EI and possibly in SCI.
Accepted papers with proper extension will be recommended to publish in the Journal of Parallel & Cloud Computing (PCC).
ORGANIZATION
General Co-chairs:
Abhinav Vishnu, Pacific Northwest National Laboratory, USA
Yinglong Xia, IBM T.J. Watson Research Center, USA
Publicity Co-chairs:
George Chin, Pacific Northwest National Laboratory, USA
Hoang Le, Sandia National Laboratories, USA
Program Committee:
Co-Chair: Neal Xiong, Colorado Technical University, USA
Co-Chair: Yihua Huang, Nanjing Universtiy, China
Vice co-chair: Makoto Takizawa, Hosei University, Japan
Vice co-chair: Ching-Hsien (Robert) Hsu, Chung Hua University, Taiwan
Vice co-chair: Jong Hyuk Park, Kyungnam University, Korea
Vice co-chair: Sajid Hussain, Nashville, Tennessee, USA
Haimonti Dutta, Columbia University, USA
Jieyue He, Southeast University, China
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Yi Wang, Tecent Holding Lt., China
Zhijun Fang, Jiangxi University of Finance and Economics, China
Wenlin Han, University of Alabama, USA
Wan Jian, Hangzhou Dianzi University, China,
Daniel W. Sun, NEC, Japan
Danny Bickson, GraphLab Inc., USA
Virendra C. Bhavsar, University of New Brunswick, Canada
Zhihui Du, Tsinghua University, China
Ichitaro Yamazaki, University of Tennessee, Knoxville, USA
KEYNOTE SPEAKER
TBD
CONTACT
Should you have any questions regarding the workshop or this webpage, please contact parlearning-AT-googlegroups.com.