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ScaDL 2020 : Scalable Deep Learning over Parallel And Distributed Infrastructures


When May 22, 2020 - May 22, 2020
Where New Orleans
Submission Deadline Feb 1, 2020
Notification Due Feb 28, 2020
Final Version Due Mar 15, 2020
Categories    parallel computing   deep learning

Call For Papers

ScaDL 2020: 2nd Workshop on Scalable Deep Learning over Parallel and Distributed Infrastructure
Colocated with IPDPS 2020

Areas of Interest
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
-Elasticity training of machine learning and deep learning jobs
-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
ScaDL 2020 accepts submissions in three categories:
Regular papers: 8-10 pages
Short papers: 4 pages
Extended abstracts: 1 page
The aforementioned lengths include all technical content, references and appendices.
Papers should be formatted using IEEE conference style, including figures, tables, and references. The IEEE conference style templates for MS Word and LaTeX provided by IEEE eXpress Conference Publishing are available for download. See the latest versions at

Submission Link

Submission deadline: Feb 1, 2020
Notifications: Feb 28, 2020
Camera Ready deadline: March 15, 2020

General Chairs
Christopher Carothers, RPI, USA
Ashish Verma, IBM Research AI, USA
Program Committee Chairs
K. R. Jayaram, IBM Research AI, USA
Parijat Dube, IBM Research AI, USA

Program Committee
Kangwook Lee, KAIST, Korea
Li Zhang, IBM Research, USA
Xiangru Lian, U Rochester, USA
Eduardo Rocha Rodrigues, IBM, Brazil
Wagner Meira Jr., UFMG, Brazil
Stacy Patterson, RPI, USA
Alex Gittens, RPI, USA
Catherine Schuman, ORNL, USA
Ignacio Blanquer, UPV, Spain
Leandro Balby Marinho, UFCG, Brazil
Chen Wang, IBM Research, USA

Publicity Chair
Danilo Ardagna, Politecnico di Milano, Italy

Steering Committee
Vijay K. Garg, University of Texas at Austin
Vinod Muthusamy, IBM Research AI
Yogish Sabharwal, IBM Research AI
Danilo Ardagna, Politecnico di Milano

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