[hpc-announce] CFP: 2nd Workshop on Scalable Data Analytics in Scientific Computing

[Piotr Luszczek]

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

The 2nd International Scalable Data Analytics in Scientific Computing
(SDASC) workshop invites submissions of original research. The event
will be co-located with the ISC High Performance 2020 conference. More
details are available at:

https://sdascconf.github.io/

With the increasing importance of methods statistical inference and
their growing use at large cloud computing facilities, both scientific
and HPC communities are looking into new ways of applying this new
computational steering approach to their large scale simulations. The
workshop will feature efforts in automated data analysis at the
convergence of computational science, HPC, and large scale data
analytics and/or inference. The focus will be on the integration of the
HPC techniques and statistical learning tasks into the modern software
stack of scalable 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 will complement the other events artificial intelligence,
machine learning, and data analytics taking place at ISC 2020.

------------------
Topics of Interest
------------------

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

-  Scientific data set creation, labeling, ingest, curation, and
    analysis with statistical inference
-  Incorporating realtime and ad-hoc data analytics into applications
    and their deployment on supercomputing and large cluster platforms
-  Computational steering through machine learning models informed by
    domain science priors
-  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 to deep neural network training, stochastic gradient
    descent, loss-function engineering, Bayesian optimization, and others
-  Model derivation and training for scalable simulations and data sets
-  Hyperparameter search and optimization for large scale training and
    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, and other deployments
-  Pretrained models' creation, use, and scaling for scientific
    simulations

We also welcome cross-cutting submissions that 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.

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

----------------------------
Additional Submission Format
----------------------------

We also intent to implement a unique format for submitting workshop
contributions: given the increasing relevance of sustainable software
development and open source community process, and acknowledging many of
the contributions in 2019 promoting software solutions, we will allow
for software submissions based on community-reviewed pull requests.
Specifically, authors can submit software contributions featuring
detailed software documentation, effectiveness and performance analysis
by pointing to a community-reviewed pull request in a versioning system.
We will complement the software review with a blind review assessing the
contribution's innovation level and community benefit, and decide on
both, scientific and software quality upon the acceptance of the
contribution.  This workflow was recently proposed as a modern
peer-reviewing concept for computer-based research. Both, traditional
contributions and software-based contributions can be submitted to
SDASC, and accepted contributions will be presented at the half-day
workshop and included in the post-conference proceedings.

The submissions are handled by Easy Chair:

https://easychair.org/my/conference.cgi?conf=sdasc2020

---------------
Important dates
---------------

- Abstract submission: March 22, 2020 (AoE)

- Full paper submission: March 29, 2020 (AoE)

- Paper acceptance: April 13, 2020

- Conference-ready deadline: May 6, 2020

- Workshop date: June 25, 2020

- Camera-ready deadline: July 5, 2020

-------------------------
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 2020 workshops' page:

https://www.isc-hpc.com/workshops-2020.html