[hpc-announce] CFP: Workshop on Scalable Data Analytics in Scientific Computing (SDASC 2021)

Piotr Luszczek luszczek at icl.utk.edu
Fri Apr 16 11:08:53 CDT 2021

.. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ..
.. Apologies if you received multiple copies of this Call for Papers.
.. Please  feel free to distribute it to interested parties.
.. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ..

Scalable Data Analytics in Scientific Computing (SDASC) workshop invites
submissions of original research. More details available at: SDASC web
site: sdascconf.github.io

The ever increasing importance of methods originating in statistical
inference and their growing use at large cloud computing facilities
leads both the scientific and HPC communities to look into new ways of
applying computational steering and incorporate it into their
large-scale simulations. The SDASC workshop will feature automated data
analysis efforts at the convergence of computational science, HPC,
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 SDASC workshop will gather experts from the intersection of
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.

Submission Guidelines

All papers must be original and cannot be simultaneously submitted to
another journal or conference or be in review process for another event.

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.

List of topics

- Scientific data set creation, ingest, curation, labelling, and
    analysis with statistical models and inference

- Incorporating realtime and ad-hoc data analytics into applications and
    their deployment on supercomputing and cluster platforms

- Computational steering through machine learning models and related
    control  theory approaches

- Meta-data and data metrics collection and generation for large data
    collections and output data sets of computational simulations

- Multi-precision training/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 with self-supervision

- 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, and related distributed optimization

- Model derivation and training for scalable simulations and data sets

- Hyperparameter search and optimization incorporating recent advances
    in Bayesian optimization

- Deployment of statistical models and their implementations such as
    TensorFlow and PyTorch or application-specific tensor frameworks.

- Integration of models with large scale simulations code bases through
    containers (Kubernetes, Docker, Singularity, OpenShift),
    virtualizaiton, colocation, and workflow frameworks

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


Program Committee

- Gabriele Cavallaro (Juelich Supercomputing Centre, Germany)

- Marat Dukhan (Google Inc., USA)

- Eileen Kūhn (Karlsruhe Institute of Technology, Germany)

- 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)

Organizing committee

- Piotr Luszczek, University of Tennessee, USA

- Hartwig Anzt, Karlsruhe Institute of Technology, Germany

Local Organizing committee

- Markus Götz (Karlsruhe Institute of Technology, Germany)

Submission Deadlines

- Submission deadline: April 29, 2021 (AoE)


The accepted papers will be published in Springer LNCS proceedings.

Manuscripts should be 12 pages maximum excluding the references. We
encourage authors to include only relevant references. Papers need to be
formatted according to Springer's single column LNCS style (see LaTeX
and Word templates).

Note: 12 pages LNCS is roughly equivalent to 6 pages in double column
IEEE format.


The workshop is co-located with ISC High Performance 2021 and will be be
hold virtually. No in-person meeting is planned.


All questions about submissions should be emailed to the address listed
on the EasyChair submission site:


More information about the hpc-announce mailing list