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Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning 2014) http://edas.info/web/parlearning2014/index.html May 23, 2014 Arizona Grand Resort PHOENIX (Arizona), USA To be held in conjunction with IPDPS 2014 (http://www.ipdps.org)
OVERVIEW 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. TOPICS Authors are invited to submit manuscripts of original unpublished research that demonstrate a strong interplay between parallel/distributed computing techniques and learning/inference applications, such as algorithm design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:
IMPORTANT DATE
PAPER SUBMISSION 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 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 |