May 29, 2017
Orlando, Florida, USA
Scaling up machine-learning (ML), data mining (DM) and reasoning algorithms from Artificial Intelligence (AI) for massive datasets is a major technical challenge in the time of "Big Data". The past ten years have seen the rise of multi-core and GPU based computing. In parallel and distributed computing, several frameworks such as OpenMP, OpenCL, and Spark continue to facilitate scaling up ML/DM/AI algorithms using higher levels of abstraction. We invite novel works that advance the trio-fields of ML/DM/AI through development of scalable algorithms or computing frameworks. Ideal submissions would be characterized as scaling up X on Y, where potential choices for X and Y are provided below.
Proceedings of the Parlearning workshop will be distributed at the conference and will be submitted for inclusion in the IEEE Xplore Digital Library after the conference.PDF Flyer
Travel awards: Students with accepted papers have a chance to apply for a travel award. Please find details on the IEEE IPDPS web page.
|ParLearning-01||ExtDict: Extensible Dictionaries for Data- and Platform-Aware Large-Scale Learning||Azalia Mirhoseini, Bita Rouhani, Ebrahim Songhori and Farinaz Koushanfar|
|ParLearning-02||Coded TeraSort||Songze Li, Sucha Supittayapornpong, Mohammad Ali Maddah-Ali and Salman Avestimehr|
|ParLearning-03||Comparing NVIDIA DGX-1/Pascal and Intel Knights Landing on Deep Learning Workloads||Nitin Gawande, Joshua Landwehr, Jeff Daily, Nathan Tallent, Abhinav Vishnu and Darren Kerbyson|
|ParLearning-04||Efficient and Portable ALS Matrix Factorization for Recommender Systems||Jing Chen, Jianbin Fang, Weifeng Liu, Tao Tang, Xuhao Chen and Canqun Yang|
|ParLearning-05||Large-Scale Stochastic Learning using GPUs||Thomas Parnell, Celestine Duenner, Kubilay Atasu, Manolis Sifalakis and Haris Pozidis|
|ParLearning-06||Distributed and in-situ machine learning for smart-homes and buildings: application to alarm sounds detection||Amaury Durand, Yanik Ngoko and Christophe Cérin|
|ParLearning-07||The New Large-Scale RNNLM System Based On Distributed Neuron||Dejiao Niu, Rui Xue, Tao Cai, Hai Li and Effah Kingsley|
|ParLearning-08||A Cache Friendly Parallel Encoder-Decoder Model without Padding on Mulit-core Architecture||Yuchen Qiao, Kenjiro Taura, Kazuma Hashimoto, Yoshimasa Tsuruoka and Akkiko Eriguchi|
Submitted manuscripts should be upto 10 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references. Format requirements are posted on the IEEE IPDPS web page.
All submissions must be uploaded electronically at https://easychair.org/conferences/?conf=parlearning2017