REPPAR 2014 - First International Workshop on Reproducibility in Parallel Computing REPPAR 2014

The workshop is concerned with experimental practices in parallel computing research. We are interested in research works that address the statistically rigorous analysis of experimental data and visualization techniques of these data. We also encourage researchers to state best practices to conduct experiments and papers that report experiences obtained when trying to reproduce or repeat experiments of others. The workshop also welcomes papers on new tools for experimental computational sciences, e.g., tools to archive large experimental data sets and the source code that generated them. This includes (1) workflow systems for defining the experimental structure of experiments and their automated execution as well as (2) experimental testbeds, which may serve as underlying framework for experimental workflows, e.g., deploying personalized operating system images on clusters.
Scope / Topics of Interest
Experimental design
correct experimental design, e.g., factorial designs
best practices how (often) to measure execution times
Experiences with experimentation
What is/was needed to reproduce/replicate/repeat experiment shown in other papers?
best practices to conduct experiments
overcoming difficulties in experimental setups, e.g., overheads introduced by tracing/profiling tools
controlling system noise on parallel machines
Experimental testbeds
reproducible deployments of virtual machines
languages to define experimental workflows
Analysis of experimental data
rigorous statistical analysis of experiments
data mining techniques to trim solution space
parallel data analysis
automated document/protocol generation
Tools for reproducible research
versioning of source code
archiving experimental data
tools for automation / re-execution of experiments
Visualization of experimental data
visualization techniques for large experimental data sets
tools for interactive data analysis of experimental results