M2R Scientific Methodology and Performance Evaluation

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Scientific Methodology and Performance Evaluation

General Informations

The coordinator for these lectures is Jean-Marc Vincent . The lecturers are Jean-Marc Vincent and Arnaud Legrand .

Lectures take place generally on Monday morning from 9:15 to 12:45 (generally, the lecture will take less than 3 hours and we will make more lectures).

The planning with lecture rooms is available at the ADE website (look for PDES and then for the Workshop on Performance Evaluation).


The aim of this course is to provide the fundamental basis for sound scientific methodology of performance evaluation of computer systems. Two approaches are developed:

  • performance measurement: based on experimental platforms (benchmarks or owner instrumented code execution), how to analyze data and synthesize performance indexes
  • performance modeling: from a description of resources and the behavior of applications, how to predict the performance of the application

Here are links to the previous editions of this lecture: 2011-2012, 2012-2013, 2013-2014.

Program and expected schedule

Using R

Installing R and Rstudio

Here is how to proceed on debian-based distributions:

sudo apt-get install r-base r-cran-ggplot2 r-cran-reshape

Rstudio and knitr are unfortunately not packaged within debian so the easiest is to download the corresponding debian package on the Rstudio webpage and then to install it manually (depending on when you do this, you can obviously change the version number).

wget http://download1.rstudio.org/rstudio-0.97.551-amd64.deb ## actually, this archive is likely to be outdated now so get the most recent one.
sudo dpkg -i rstudio-0.97.551-amd64.deb
sudo apt-get -f install # to fix possibly missing dependencies

You will also need to install knitr. To this end, you should simply run R (or Rstudio) and use the following command.


If r-cran-ggplot2 or r-cran-reshape could not be installed for some reason, you can also install it through R by doing:


Producing documents

The easiest way to go is probably to use R+Markdown (Rmd files) in Rstudio and to export them via Rpubs to make available whatever you want.

We can roughly distinguish between three kinds of documents:

  1. Lab notebook (with everything you try and that is meant mainly for yourself)
  2. Experimental report (selected results and explanations with enough details to discuss with your advisor)
  3. Result description (rather short with only the main point and, which could be embedded in an article)

We expect you to provide us the last two ones and to make them publicly available so as to allow others to comment on them.


For a quick start, you may want to look at R for Beginners (French version). A probably more entertaining way to go is to follow a good online lecture providing an introduction to R and to data analysis such as this one: https://www.coursera.org/course/compdata