Dans la même collectionPUF/JLPC Summer school on performance Metrics, Modeling and Simulation of Large HPC Systems : ... Simulation of HPC systems part 1 Simulation of HPC systems part 2 Simulation of HPC systems part 3 Best Practices for Reproducible Research part 2 Instrumentation and Analysis part 1
Best Practices for Reproducible Research part 1
The aim of this tutorial is to sensibilize the audience to the experiment and analysis reproducibility issue in particular in computer science. I will present tools that help answering the analysis problem and may also reveal useful for managing the experimental process through notebooks.
More precisely, I will introduce the audience to the following tools:
- R and ggplot2 that provide a standard, efficient and flexible data management and graph generation mechanism. Although R is quite cumbersome at first for computer scientists, it quickly reveals an incredible asset compared to spreadsheets, gnuplot or graphical libraries like matplotlib or tikz.
- knitR is a tool that enables to integrate R commands within a LaTeX or a Markdown document. It allows to fully automatize data post-processing/analysis and figure generation down to their integration to a report. Beyond the gain in term of ease of generation, page layout, uniformity insurance, such integration allows anyone to easily check what has been done during the analysis and possibly to improve graphs or analysis.
- I will explain how to use these tools with Rstudio, which is a multi-platform and easy-to-use IDE for R. For example, using R+Markdown (Rmd files) in Rstudio, it is extremely easy to export the output result to Rpubs and hence make the result of your research available to others in no more than two clicks.
- I will also mention other alternatives such as org-mode and babel or the ipython notebook that allow a day-to-day practice of reproducible research in a somehow more fluent way than knitR but is mainly a matter of taste.
Depending on the question of the audience, I can also help the attendees analyzing some of their data and introduce them to the basics of data analysis.
- Label UNT : Unisciel
- Date de réalisation : 12 Juin 2014
- Durée du programme : 106 min
- Classification Dewey : Computer Science
- Auteur(s) : LEGRAND Arnaud
- Editeur : University of Illinois at Urbana-Champaign , INRIA (Institut national de recherche en informatique et automatique) , Argonne National Laboratory , Illinois' Center for Extreme-Scale Computation , National Center for Supercomputing Applications , Barcelona Supercomputer Center
- Langue : Anglais
- Mots-clés : high performance computing
- Conditions d’utilisation / Copyright : Document libre, dans le cadre de la licence Creative Commons (http://creativecommons.org/licenses/by-nd/2.0/fr/), citation de l'auteur obligatoire et interdiction de désassembler (paternité, pas de modification)