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 2021 conference. More details are available at:
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.
This workshop will complement the other events artificial intelligence, machine learning, and data analytics taking place at ISC 2021.
Topics of Interest¶
A list of topics of interest for speakers and attendees include:
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 techniques
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.
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 publication.
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:
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.
Important dates¶
Full paper submission deadline: April 29, 2021 (AoE)
Paper acceptance: May 15, 2021
Conference-ready deadline: May 30, 2021
Workshop date: June 24, 2021
Camera-ready deadline: July 15, 2021
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:
and on the ISC 2021 workshops’ page: