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