[hpc-announce] all for Papers: ScaDL - Scalable Deep Learning over Parallel And Distributed Infrastructures

[Scalable DeepLearning]

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

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
https://www.ieee.org/conferences/publishing/templates.html

Submission Link: https://easychair.org/conferences/?conf=scadl2020

Deadlines:
Submission deadline:       Feb  1, 2020
Notifications:             Feb 28, 2020
Camera Ready deadline:     Mar 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

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

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