Apologies for the potential duplication.
-Walid
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CALL FOR PAPERS
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Computational Reproducibility at Exascale Workshop (CRE2017) ------------------------------------------------------------
Where: In cooperation with SC17, Denver, Colorado When: Sunday afternoon, November 12, 2017 Web: http://www.cs.fsu.edu/~cre Submit: https://easychair.org/conferences/?conf=cre2017 Deadline: Monday, August 28, 2017 Notifications: Monday, September 18, 2017 Full Papers: Monday, October 02, 2017 Organized by: Walid Keyrouz (NIST), Miriam Leeser (NEU), and Michael Mascagni (FSU & NIST)
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This workshop will address the problems of reproducibility in HPC in general and those anticipated as we scale to Exascale machines in the next decade. We seek contributions of extended abstracts (two pages) in the areas of computational reproducibility in HPC from academic, government, and industry stakeholders. Areas of interest include, but are not limited to:
- Case studies of reproducibility or the lack of thereof - Reproducibility issues in current HPC - System-level solutions - Algorithmic solutions - Software solutions - Uncertainty quantification in computational reproducibility - Fundamental numerical analysis of reproducibility - Future prospects
Papers submitted to the workshop will be reviewed and the top papers will be selected to be presented at the workshop. In addition, a group of papers will be published in a special issue of the International Journal of High-Performance Computing and Applications (IJHPCA) devoted to Computational Reproducibility. Please note that papers submitted to the IJHPCA for the CRE2017 special issue must fall within the IJHPCA's editorial scope. This primarily means that all papers for the special issue must have relevance to high-performance computing.
Overview and Background =======================
Experimental reproducibility is a cornerstone of the scientific method. As computing has grown into a powerful tool for scientific inquiry, computational reproducibility has been one of the core assumptions underlying scientific computing. With "traditional" single-core CPUs, documenting a numerical result was relatively straightforward. However, hardware developments over the past several decades have made it almost impossible to ensure computational reproducibility or to even fully document a computation without incurring a severe loss of performance. This loss of reproducibility started when systems combined parallelism (e.g., clusters) with non-determinism (e.g., single-core CPUs with out-of-order execution). It has accelerated with recent architectural trends towards platforms with increasingly large numbers of processing elements, namely multicore CPUs and compute accelerators (GPUs, Intel Xeon Phi, FPGAs).
Programmers targeting these platforms rely on tools and libraries to produce codes or execute them efficiently. As a result, codes can run efficiently, but have execution details that can be impossible to predict and are often very difficult to understand after execution. Furthermore, parallel implementations often result in code with varying execution orders between runs, leading to non-reproducible computations. The underlying reasons are that (1) the hardware and system software allocate parallel work in ways that are not always specifiable at compile time and (2) the execution often proceeds in an opportunistic manner with the execution order changing between runs. As such, floating-point computations, which are not commutative and associative, can have different execution orders and execute on different processing elements between runs, leading to runs with varying results as a matter of fact. The predictability of systems is further complicated by two issues that are becoming more critical as systems grow in scale: (1) interconnect systems with latencies that are often outside the control of programmers and (2) reliability as the mean time between failure (MTBF) is now measured in hours on large systems. This situation seriously affects the ability to rely on scientific computations as a metrological substitute for experimentation.
This workshop extends the Numerical Reproducibility at Exascale Workshops (conducted in 2015 and 2016 at SC) to address the broader range of issues in reproducibility that arise when computing at Exascale. The first edition, NRE2015 was held at SC15, its webpage can be found here: http://www.nist.gov/itl/ssd/is/numreprod2015.cfm. The second edition, NRE2016, was at SC16 and its webpage can be found here: http://www.cs.fsu.edu/~cre/nre-2016.html.
Submissions ===========
Submissions of two page extended abstracts are sought. The format for the abstracts should follow the IEEE Conference Proceedings format. Templates are available at "IEEE - Manuscript Templates for Conference Proceedings" (https://www.ieee.org/conferences_events/conferences/publishing/templates.htm...). The full papers must be in the format of the International Journal of High-Performance Computing and Applications (IJHPCA) (https://us.sagepub.com/en-us/nam/manuscript-submission-guidelines).
The abstracts are to submitted as a PDF document using Easychair at https://easychair.org/conferences/?conf=cre2017
Important Dates (all are Mondays) =================================
Aug. 28, 2017: submission deadline for two page abstracts via https://easychair.org/conferences/?conf=cre2017
Sep. 18, 2017: notification of authors about their submissions based on rejection, acceptance as a paper, acceptance as a paper and presentation
Oct. 02, 2017: submission deadline for full papers for refereeing via the IJHPCA site, the papers must be in IJHPCA format
Organizers and Co-Editors of the IJHPCA Special Issue =====================================================
- Walid Keyrouz, National Institute of Standards and Technology (NIST), USA - Miriam Leeser, Northeastern University, USA - Michael Mascagni, National Institute of Standards and Technology (NIST) and Florida State University, USA
Steering Committee ==================
- Dong H. Ahn, Lawrence Livermore National Lab, USA - David Bailey, UC Davis, USA - Mike Heroux, Sandia National Laboratory, USA - Torsten Hoefler, ETH-Zurich, Switzerland - Walid Keyrouz (co-organizer), NIST, USA - Miriam Leeser (co-organizer), Northeastern University, USA - Xiaoye Sherry Li, Lawrence Berkeley National Laboratory, USA - Yaohang Li, Old Dominion University, USA - Michael Mascagni (co-organizer), FSU/NIST, USA - Junji Nagano, Institute of Statistical Mathematics, Japan - Nathalie Revol, INRIA/ENS-Lyon, France - Siegfried Rump, University of Hamburg, Germany - Michela Taufer, University of Delaware
Contact =======
E-mail: numerical.reproducibility.at.nist.gov (replace ".at." by "@")
-Walid
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Walid Keyrouz, PhD Research Scientist NIST | ITL | SSD