|Where||In cooperation with SC18, Dallas, TX|
|When||Sunday afternoon, November 11, 2018|
|Submit||http://bit.ly/2KLz4zM or long-url|
|Deadline||Tuesday, September 04, 2018, at 11:59 pm EDT|
|Notifications||Monday, September 17, 2018|
|Full Papers||Monday, November 19, 2018|
|Organized by||Walid Keyrouz (NIST), Miriam Leeser (NEU), and Michael Mascagni (FSU & NIST)|
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 combines the Numerical Reproducibility at Exascale workshops (conducted in 2015 and 2016 at SC) and the panel on Reproducibility held at SC’16 (originally a BOF at SC’15) to address several different issues in reproducibility that arise when computing at Exascale. The first combined workshop, Computational Reproducibility at Exascale 2017 (CRE2017), took place at SC17 and was a success; its web page can be found here. Previous editions of the workshop were NRE2015 and NRE2016; their web pages can be found here and here respectively.
The workshop is meant to address issues of numerical reproducibility as well as approaches and best practices to sharing and running code and the reproducible dissemination of computational results. The workshop is meant to address the scope of the problems of computational reproducibility in HPC in general, and those anticipated as we scale up to Exascale machines in the next decade. The participants of this workshop will include government, academic, and industry stakeholders; the goals of this workshop are to understand the current state of the problems that arise, what work is being done to deal with these issues, and what the community thinks the possible approaches to these problems are.
The workshop is meant to address the scope of the problems of numerical reproducibility in HPC in general and those anticipated as we scale to Exascale machines in the next decade. We initially 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:
The workshop will have: (1) two plenary talks (25 min + 5 min Q&A each), (2) four contributed talks (15 min + 5 min Q&A each), and (3) a 40-min panel discussion to summarize the problem, current research, and prospects on long-term solutions. The table below gives the workshop’s schedule:
|2:00 pm|| |
Victoria Stodden (UIUC)
|2:30 pm|| |
|2:50 pm|| |
|3:10 pm|| |
|3:30 pm|| |
Food and coffee provided by SC
|4:00 pm|| |
Bert Debusschere (Sandia)
|4:30 pm|| |
|4:50 pm|| |
Panel Discussion on Computational Reproducibility: Dong H. Ahn (LLNL), Allison Baker (UCAR), Neil Burgess (ARM), Jos Martin (Mathworks), and Michela Taufer (UTK)
Papers submitted to the workshop will be reviewed. The referees will select the papers that will be presented in 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 CRE2018 special issue must fall within the IJHPCA’s editorial scope. This primarily means that all papers for the special issue have relevance to high-performance computing.
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.
The abstracts are to be submitted as a PDF document using the Supercomputing submission site at http://bit.ly/2KLz4zM or long-url.
Instructions for submitting the full-length papers are given below.
The authors of accepted presentations at the workshop are invited to submit a full-length paper, consisting of up to 12 pages, to a Special Issue of the International Journal of High-Performance Computing and Applications (IJHPCA) at http://mc.manuscriptcentral.com:80/ijhpca. Authors need to submit the following:
Here are the journal’s formatting and submission constraints:
|Tue., Sep. 04, 2018:||submission due for 2-page abstracts via http://bit.ly/2KLz4zM|
|Mon., Sep. 17, 2018:||notification of authors about their submissions based on rejection, acceptance as a paper, acceptance as a paper and presentation|
|Mon., Nov. 19, 2018:||submission deadline for full papers for refereeing via the IJHPCA site, the papers must be in the IJHPCA format|
E-mail: numerical.reproducibility.at.nist.gov (replace “.at.” by “@”)