REPPAR 2017 - 4th International Workshop on Reproducibility in Parallel Computing (REPPAR)
Topics/Call fo Papers
4th International Workshop on Reproducibility in Parallel Computing (REPPAR)
http://reppar.org
in conjunction with IPDPS 2017 (June 2, 2017), Orlando, FL
===
1 Scope
===
The workshop is focused on the design, implementation, execution, and
analysis of experiments in parallel and distributed computing
(including HPC, Clouds, Networking, Big Data) to improve the
reproducibility of results.
2 Keywords
===
Reproducibility, Parallel Computing/HPC, Open Science, Data
Provenance, Scientific Workflows
3 Topics addressed in the workshop include:
===
- Experimental design of parallel computing experiments
- Experiences and best practices for conducting experiments (including
papers that address the reproduction of other articles)
- Supporting reproducibility in experimental testbeds for parallel
computing
- Tools for reproducible research (e.g., control of experiments,
versioning, archiving)
- Analysis of experimental data (e.g., visualization, statistical
analysis, provenance)
- Automated uncertainty quantification for experimental workflows (or
data-focused workflows)
- Systems to incorporate (potentially very large) data into automated
testing frameworks
- Sustainable models for public data sharing
- Ideas on artifact evaluation in Distributed Computing and HPC
- Improving the review process in Parallel Computing
- Open Science and Parallel Computing
- Performance non-regression testing
4 Paper Types
===
- full papers (10 pages)
- short papers (8 pages)
5 Demos and Tutorials
===
We welcome demos and tutorials addressing (potential) solutions to the
reproducibility (replicability) problem. Please contact us directly
if you want to give a tutorial (demo).
6 Important Dates
===
- Workshop papers due: 13 January 2017
- Workshop author notification: 17 February 2017
- Workshop camera-ready papers due: 15 March 2017
- Demos/Tutorials proposals due: 10 February 2017
7 Proceedings
===
Accepted manuscripts will be included in the IPDPS workshop
proceedings.
8 Submission
===
Submissions are handled through the EasyChair conference system:
[https://easychair.org/conferences/?conf=reppar2017]
9 Paper Style
===
Submitted manuscripts may not exceed ten (10) single-spaced
double-column pages using 10-point size font on 8.5x11 inch pages
(IEEE conference style), including figures, tables, and
references. The submitted manuscripts should include author names and
affiliations. See the LaTeX style template for details:
[http://www.ipdps.org/templates/IEEECS_CPS_LaTeX_Le...]
10 Organizers
===
- Sascha Hunold, TU Wien, Austria
- Arnaud Legrand, CNRS, LIG Grenoble, France
- Lucas Nussbaum, CNRS, LORIA, France
11 Program Committee
===
- Louis-Claude Canon, University of Franche-Comté, France
- Lionel Eyraud-Dubois, INRIA Bordeaux, France
- Swann Perarnau, Argonne National Lab, USA
- Robert Ricci, University of Utah, USA
- Mark Stillwell, Cisco Meraki, UK
http://reppar.org
in conjunction with IPDPS 2017 (June 2, 2017), Orlando, FL
===
1 Scope
===
The workshop is focused on the design, implementation, execution, and
analysis of experiments in parallel and distributed computing
(including HPC, Clouds, Networking, Big Data) to improve the
reproducibility of results.
2 Keywords
===
Reproducibility, Parallel Computing/HPC, Open Science, Data
Provenance, Scientific Workflows
3 Topics addressed in the workshop include:
===
- Experimental design of parallel computing experiments
- Experiences and best practices for conducting experiments (including
papers that address the reproduction of other articles)
- Supporting reproducibility in experimental testbeds for parallel
computing
- Tools for reproducible research (e.g., control of experiments,
versioning, archiving)
- Analysis of experimental data (e.g., visualization, statistical
analysis, provenance)
- Automated uncertainty quantification for experimental workflows (or
data-focused workflows)
- Systems to incorporate (potentially very large) data into automated
testing frameworks
- Sustainable models for public data sharing
- Ideas on artifact evaluation in Distributed Computing and HPC
- Improving the review process in Parallel Computing
- Open Science and Parallel Computing
- Performance non-regression testing
4 Paper Types
===
- full papers (10 pages)
- short papers (8 pages)
5 Demos and Tutorials
===
We welcome demos and tutorials addressing (potential) solutions to the
reproducibility (replicability) problem. Please contact us directly
if you want to give a tutorial (demo).
6 Important Dates
===
- Workshop papers due: 13 January 2017
- Workshop author notification: 17 February 2017
- Workshop camera-ready papers due: 15 March 2017
- Demos/Tutorials proposals due: 10 February 2017
7 Proceedings
===
Accepted manuscripts will be included in the IPDPS workshop
proceedings.
8 Submission
===
Submissions are handled through the EasyChair conference system:
[https://easychair.org/conferences/?conf=reppar2017]
9 Paper Style
===
Submitted manuscripts may not exceed ten (10) single-spaced
double-column pages using 10-point size font on 8.5x11 inch pages
(IEEE conference style), including figures, tables, and
references. The submitted manuscripts should include author names and
affiliations. See the LaTeX style template for details:
[http://www.ipdps.org/templates/IEEECS_CPS_LaTeX_Le...]
10 Organizers
===
- Sascha Hunold, TU Wien, Austria
- Arnaud Legrand, CNRS, LIG Grenoble, France
- Lucas Nussbaum, CNRS, LORIA, France
11 Program Committee
===
- Louis-Claude Canon, University of Franche-Comté, France
- Lionel Eyraud-Dubois, INRIA Bordeaux, France
- Swann Perarnau, Argonne National Lab, USA
- Robert Ricci, University of Utah, USA
- Mark Stillwell, Cisco Meraki, UK
Other CFPs
- IEEE Workshop on Parallel and Distributed Processing for Computational Social Systems (ParSocial 2017)
- The Twelfth International Workshop on Automatic Performance Tuning
- 2017 4th Annual Chapel Implementers and Users Workshop
- 2017 IEEE International Workshop on High-Performance Big Data Computing
- 19th Workshop on Advances in Parallel and Distributed Computational Models
Last modified: 2016-11-16 12:04:33