Please note the first workshop on ScaDL (Scalable Deep Learning over Parallel and Distributed Infrastructures) is being organized with IPDPS 2019 in Rio de Janeiro, Brazil. It will provide a unique forum for researchers from academia as well as industry working at the intersection of large scale systems and deep learning to discuss the most recent advances in this area. Please consider submitting your recent work to this forum.
Call for Papers
In this workshop we solicit research papers focused on distributed deep learning aiming to achieve efficiency and scalability for deep learning jobs over distributed and parallel systems. Papers focusing both on algorithms as well as systems are welcome. We invite authors to submit papers on topics including but not limited to:
Deep learning on HPC systems
Deep learning for edge devices
Model-parallel and data-parallel techniques
Asynchronous SGD for Training DNNs
Communication-Efficient Training of DNNs
Model/data/gradient compression
Learning in Resource constrained environments
Coding Techniques for Straggler Mitigation
Elasticity for deep learning jobs/spot market enablement
Hyper-parameter tuning for deep learning jobs
Hardware Acceleration for Deep Learning
Scalability of deep learning jobs on large number of nodes
Deep learning on heterogeneous infrastructure
Efficient and Scalable Inference
Data storage/access in shared networks for deep learning jobs
Author Instructions
Submitted manuscripts may not exceed ten (10) single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references. The submitted manuscripts should include author names and affiliations.
The IEEE conference style templates for MS Word and LaTeX provided by IEEE eXpress Conference Publishing are available for download. See the latest versions here.
Use the following link for submissions: https://easychair.org/conferences/?conf=scadl2019
Organizing Committee
General Chairs
Gauri Joshi, Carnegie Mellon University (gaurij@andrew.cmu.edu)
Ashish Verma, IBM Research AI (ashish.verma1@us.ibm.com)
Program Chairs
Yogish Sabharwal, IBM Research AI
Parijat Dube, IBM Research AI
Local Chair
Eduardo Rodrigues, IBM Research
Steering Committee
Vijay K. Garg, University of Texas at Austin
Vinod Muthuswamy, IBM Research AI
Technical Program Committee
Alvaro Coutinho - Federal University of Rio de Janeiro
Dimitris Papailiopoulos, University of of Wisconsin-Madison
Esteban Meneses, Costa Rica Institute of Technology
Kangwook Lee, KAIST
Li Zhang, IBM Research
Lydia Chen, TU Delft
Philippe Navaux, University of Rio Grande do Sul
Rahul Garg, Indian Institute of Technology Delhi
Vikas Sindhwani, Google Brain
Wei Zhang, IBM Research
Xiangru Lian, University of Rochester
Key Dates
Paper Submission January 25, 2019
Acceptance Notification February 25, 2019
Camera-ready due March 15, 2019
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