Introduction to High-Performance Computing with R - UseR ...

Why Measure Vector Ra BLAS/GPUs Compile Parallel Automation
Introduction to
High-Performance Computing with R
UseR! 2009 Tutorial
Dirk Eddelbuettel, Ph.D.
Dirk.Eddelbuettel@R-Project.org
edd@debian.org
Université Rennes II, Agrocampus Ouest
Laboratoire de Mathématiques Appliquées
7 July 2009
Dirk Eddelbuettel Intro to High-Performance R / UseR! 2009 TutorialWhy Measure Vector Ra BLAS/GPUs Compile Parallel Automation
Motivation: What describes our current situation?
Moore’s Law: Computers
keep getting faster and
faster
But at the same time our
datasets get bigger and
bigger.
So we’re still waiting and
waiting . . .
Hence: A need for higher
performance computing with
R.
Source: http://en.wikipedia.org/wiki/Moore’s_law
Dirk Eddelbuettel Intro to High-Performance R / UseR! 2009 TutorialWhy Measure Vector Ra BLAS/GPUs Compile Parallel Automation
Motivation: Presentation Roadmap
We will start by measuring how we are doing before looking at ways
to improve our computing performance.
We will look at vectorisation, as well as various ways to compile code.
We will look briefly at debugging tools and tricks as well.
We will have a detailed discussion of several ways to get more things
done at the same time by using simple parallel computing
approaches.
We also look at ways to automate and script running R code.
Dirk Eddelbuettel Intro to High-Performance R / UseR! 2009 TutorialWhy Measure Vector Ra BLAS/GPUs Compile Parallel Automation
Table of Contents
1 Motivation
2 Measuring ...