[hpc-announce] CFP: 1st Workshop on Scalable Data Analytics in Scientific Computing

[Piotr Luszczek]

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===============
Call for Papers
===============

The 1st workshop Scalable Data Analytics in Scientific Computing
(SDASC) invites submissions of original research.

The event will be co-located with ISC High Performance 2019 conference.

More details available at:

https://sdascconf.github.io/

Submission site:

https://easychair.org/my/conference.cgi?conf=sdasc2019



With the increasing importance of methods originating in statistical
inference and their growing use at large cloud computing facilities,
both scientific and HPC communities are looking into new ways of
applying computational steering to their large scale simulations. The
workshop will feature automated data analysis efforts at the convergence
of computational science, HPC, and large scale data analytics and
inference. The focus will be on the integration of the HPC techniques
and statistical learning tasks into the modern software stack of
computational science.

The half-day SDASC workshop will gather experts from the computational
science, HPC, and machine learning communities. The committee members
are recognized in their respective fields as experts of note and will
assure fulfilment of the goals of the workshop.

This workshop could also be considered as a complementary event to the
Machine Learning Learning Day slated to take place at ISC 2019 that is
becoming a mainstay of the event.


==================
Topics of Interest
==================

A list of topics of interest for speakers and attendees include:

-  Scientific data set creation, ingest, curation, and analysis with
    statistical inference
-  Incorporating realtime and ad-hoc data analytics into applications
    and their deployment on supercomputing and cluster platforms
-  Computational steering through Machine Learning models
-  Meta-data and data metrics collection and generation for large data
    collections and output data sets of computational simulations
-  Multi-precision inference methods and their use on modern hardware
    for simulation data
-  Novel use of discriminative and generative Machine Learning
    approaches for scientific data sets including Adversarial and
    Reinforcement Learning
-  Modern HPC storage issues when dealing with integration of
    computational simulation outputs with data analytics software
-  Synchronous and asynchronous learning approaches at scale  for
    methods related deep neural network training, stochastic gradient
    descent, loss-function engineering, etc.
-  Model derivation and training for scalable simulations and data sets
-  Hyperparameter search and optimization for large scale inference
-  Deployment of statistical models and their implementations such as
    TensorFlow, (Py)Torch, Caffe 1/2, Keras, MxNet combined with their
    integration with large scale simulations through containers
    (Kubernetes, Docker, Singularity, OpenShift), virtualizaiton,
    colocation, etc.

We also welcome cross-cutting submissions that are span some of the
topics mentioned above.

=====================
Submission guidelines
=====================

The workshop will use single-blind peer review.  The submitted
manuscripts will be reviewed anonymously but the authors will be known
to the reviewers. Submissions will be scored on the following criteria:
originality, technical strength and correctness as well as significance,
quality of presentation, and relevance to the workshop topics of
interest.

With respect to originality: the submitted manuscripts should have _NOT_
appeared in at another venue such as conference, workshop, symposium, or
published in a journal.  Also, the manuscript should _NOT_ be under
consideration for another such a venue or journal.

The accepted papers will be published in Springer proceedings (see
below for deadlines, dates, and format).

Manuscripts will be 12 pages maximum excluding the references (we
encourage authors to include relevant references).
Papers need to be formatted according to Springer's single column LNCS
style (see http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0
for LaTeX and Word templates).  Note: 12 pages LNCS is roughly
equivalent to 6 pages in double column IEEE format.

The submissions are handled by Easy Chair:

https://easychair.org/my/conference.cgi?conf=sdasc2019

===============
Important dates
===============

- Abstract submission: April 1, 2019 (AoE)

- Full paper submission: April 8, 2019 (AoE)

- Paper acceptance: May 6, 2019

- Conference-ready deadline: June 6, 2019

- Workshop date: June 20, 2019

- Camera-ready deadline: July 22, 2019

=========================
Organizers (alphabetical)
=========================

* Hartwig Anzt, Karlsruhe Institute of Technology, Germany
* Gabriele Cavallaro, Juelich Supercomputing Centre, Germany
* Marat Dukhan, Google Inc., USA
* Markus Götz, Karlsruhe Institute of Technology, Germany
* Eileen Kūhn, Karlsruhe Institute of Technology, Germany
* Piotr Luszczek, University of Tennessee, USA
* Daniel Jacobson, Oak Ridge National Laboratory, USA
* Xipeng Shen, North Carolina State University, USA
* Martin Siggel, German Aerospace Center /DLR/ Cologne, Germany
* Misha Smelyanskiy, Facebook Inc., USA
* Miroslav Stoyanov, Oak Ridge National Laboratory, USA

More details available at:

https://sdascconf.github.io/

and on the ISC 2019 workshops' page:

https://www.isc-hpc.com/workshops-2019.html