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

May 23, 2014

Arizona Grand Resort PHOENIX (Arizona), USA

To be held in conjunction with IPDPS 2014 (



This workshop is one of the major meetings for bringing together researchers in High Performance Computing and Artificial Intelligence (Machine Learning, Data Mining, BigData Analytics, etc.) to discuss state-of-the-art algorithms, identify critical applications that benefit from parallelization, prospect research areas that require most convergence and assess the impact on broader technical landscape.

Data-driven computing needs no introduction today. However, the growth in volume and heterogeneity in data seems to outpace the growth in computing power. 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.

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.


Authors are invited to submit manuscripts of original unpublished research that demonstrate a strong interplay between parallel/distributed computing techniques and learning/inference applications, such as algorithm design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


    Workshop Paper Due

    December 30, 2013

    Author Notification

    February 14, 2014

    Camera-ready Paper Due

    March 14, 2014


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 ( shortly after the author notification Authors can purchase up to 2 additional pages for camera-ready papers after acceptance. Please find details on Students with accepted papers have a chance to apply for a travel award. Please find details at

Submit your paper using EDAS portal for ParLearning:


    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