The 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics

May 29, 2017
Orlando, Florida, USA

In Conjunction with 31st IEEE International Parallel & Distributed Processing Symposium
May 29-June 2, 2017
Buena Vista Palace Hotel
Orlando, Florida USA
IPDPS 2017 logo

Call for Papers

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.

Scaling up


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

Journal publication

Selected papers from the workshop will be published in a Special Issue of Future Generation Computer Systems, Elsevier's International Journal of eScience. Special Issue papers will undergo additional review.


Best Paper Award: The program committee will nominate a paper for the Best Paper award. In past years, the Best Paper award included a cash prize. Stay tuned for this year!

Travel awards: Students with accepted papers have a chance to apply for a travel award. Please find details on the IEEE IPDPS web page.

Advance Program

Paper ID Title Authors
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 CĂ©rin
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

Important Dates

Paper Guidelines

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


Technical Program Committee

Past workshops