[hpc-announce] Call for Papers - DIDL 2018 : Second Workshop on Distributed Infrastructures for Deep Learning

[Vatche Ishakian]

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Second Workshop on Distributed Infrastructures for Deep Learning (DIDL) 2017
Rennes, France
Dec 10-14, 2018
https://didl-conf.github.io/


 Deep learning is a rapidly growing field of machine learning, and has
proven successful in many domains, including computer vision, language
translation, and speech recognition. The training of deep neural networks
is resource intensive, requiring compute accelerators such as GPUs, as well
as large amounts of storage and memory, and network bandwidth.
Additionally, getting the training data ready requires a lot of tooling for
data cleansing, data merging, ambiguity resolution, etc. Sophisticated
middleware abstractions are needed to schedule resources, manage the
distributed training job as well as visualize how well the training is
progressing. Likewise, serving the large neural network models with low
latency constraints can require middleware to manage model caching,
selection, and refinement.

All the major cloud providers, including Amazon, Google, IBM, and Microsoft
have started to offer cloud services in the last year or so with services
to train and/or serve deep neural network models. In addition, there is a
lot of activity in open source middleware for deep learning, including
Tensorflow, Theano, Caffe2, PyTorch, and MXNet. There are also efforts to
extend existing platforms such as Spark for deep learning workloads.

This workshop focuses on the tools, frameworks, and algorithms to support
executing deep learning algorithms in a distributed environment. As new
hardware and accelerators become available, the middleware and systems need
to be able exploit their capabilities and ensure they are utilized
efficiently.

Authors are invited to submit research papers, experience papers,
demonstrations, or position papers
Topics
This workshop solicits papers from both academia and industry on the state
of practice and state of the art in deep learning infrastructures. Topics
of interest include but are not limited to:

    Resource scheduling algorithms for deep learning workloads
    Advances in deep learning frameworks
    Programming abstractions for deep learning models
    Middleware support for hardware accelerators
    Novel distribution techniques for training large neural networks
    Case studies of deep learning middleware
    Optimization techniques for Inferencing
    Novel debugging and logging techniques
    Data cleansing, data disambiguation tools for deep learning
    Data visualization tools for deep learning

Dates and location
Paper submissions: August 31, 2018
Notification to authors: September 28, 2018
Camera-ready copy due: October 19, 2018

The DIDL workshop is co-located with the Middleware conference, which will
be held in Rennes, France from December 10-14th 2018.
Papers and Submissions
We are looking for the following types of submissions:

   Research and industry papers (up to 6 pages): Reports on original
results including novel techniques, significant case studies or surveys.
Authors may include extra material beyond the six pages as a clearly marked
appendix, which reviewers are not obliged to read but could read.
   Position papers (up to 4 pages): Reports identifying unaddressed
problems and research challenges.
   Abstracts (up to 1 page): An extended abstract on a preliminary or
ongoing work.

Papers must be written in English and submitted in PDF format. All papers
should follow ACM formatting instructions, specifically the ACM SIG
Proceedings Standard Style. The author kit containing the templates for the
required style can be found at
http://www.acm.org/publications/proceedings-template.

Submissions should not be blinded for review. Please submit your papers via
the submission site: https://didl18.hotcrp.com/

All accepted papers will appear in the Middleware 2017 companion
proceedings, available in the ACM Digital Library. All accepted papers will
also be presented at the workshop, and at least one author of each paper
must register for the workshop.
Workshop Co-chairs

Bishwaranjan Bhattacharjee, IBM Research
Vatche Ishakian, Bentley University
Hans-Arno Jacobsen, Middleware Systems Research Group
Vinod Muthusamy, IBM Research
Program Committee

Ian Foster, Argonne National Laboratory and the University of Chicago
Benoit Huet, Eurecom
Pietro Michiardi, Eurecom
Peter Pietzuch, Imperial College
Evgenia Smirni, College of William and Mary
Yandong Wang, Citadel Securities
Chuan Wu, University of Hong Kong
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