*ParLearning 2014 - CFP*
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 <http://www.ipdps.org/>
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 <http://edas.info/web/
version<http://edas.info/web/
*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: Yinglong Xia, IBM T.J. Watson Research Center, 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, NICTA, Australia
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
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Lawrence Holder, Washington State University, USA
Vinod Tipparaju, AMD, USA
Nishkam Ravi, NEC Labs, USA
Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS),
Brazil
*KEYNOTE SPEAKER*
TBD
*CONTACT*
Should you have any questions regarding the workshop or this webpage,
please contact parlearning ~AT~ googlegroups DOT com.
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 <http://www.ipdps.org/>
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 <http://edas.info/web/
version<http://edas.info/web/
*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: Yinglong Xia, IBM T.J. Watson Research Center, 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, NICTA, Australia
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
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Lawrence Holder, Washington State University, USA
Vinod Tipparaju, AMD, USA
Nishkam Ravi, NEC Labs, USA
Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS),
Brazil
*KEYNOTE SPEAKER*
TBD
*CONTACT*
Should you have any questions regarding the workshop or this webpage,
please contact parlearning ~AT~ googlegroups DOT com.
No comments:
Post a Comment