[hpc-announce] ScaDL workshop at IPDPS'19

Scalable DeepLearning scadl.pdi at gmail.com
Tue Jan 15 00:03:06 CST 2019


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

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

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:

General Chairs
Gauri Joshi, Carnegie Mellon University (gaurij at andrew.cmu.edu)
Ashish Verma, IBM Research AI (ashish.verma1 at us.ibm.com)

Yogish Sabharwal, IBM Research AI
Parijat Dube, IBM Research AI

Eduardo Rodrigues, IBM Research

Vijay K. Garg, University of Texas at Austin
Vinod Muthuswamy, IBM Research AI

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

Paper Submission            January   25, 2019
Acceptance Notification     February  25, 2019
Camera-ready due            March     15, 2019

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