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Description Participants Agenda (pdf) Videos

NIMBioS/XSEDE/NICS Tutorial

Using R for HPC

Cluster image.

Tutorial PDFs to download:

Setup/Agenda
Attendee info
Introduction
Ex1: Basics
Ex2: Performance
Ex3: Interfacing to compiled code
Ex4: Parallelism

Twitter hashtag: #learnR

Topic: An In-Depth Introduction to Using R for High Performance Computing

Meeting dates: February 27, 2015, 1–5 p.m. EST

Location: NIMBioS at the University of Tennessee, Knoxville

Organizers:
Drew Schmidt
    Extreme Science and Engineering Discovery Environment (XSEDE)
    National Institute for Computational Sciences (NICS)
Eric Carr, NIMBioS

Objectives: By some measures, R is the most popular software package for the analysis of data. But R has a reputation for being sluggish and inappropriate for large datasets. However, much of R's problems with performance and scalability are due to bad practices of individual programmers rather than being inherent limitations of R itself.

This half-day (four hour) tutorial, introduced participants to debugging, profiling and performance analysis, optimization, foreign language API's, and parallel programming with R. There was also a comprehensive hands-on component to reinforce topics introduced during the lecture portion.

Participants: The tutorial was ideally suited for those already working with R, as well as service providers serving R customers. The content was appropriate for any students, researchers, or staff working with R and interested in performance.

Descriptive Flyer

The tutorial was live streamed and also had space for local participants. A live chat took place via Twitter with the hashtag #learnR. Presentation videos were archived and are available for online viewing at the link below.

Video Playlist

Evaluation Report

This tutorial was a joint training between the University of Tennessee, NIMBioS, XSEDE, and NICS.

UT logo. NIMBioS logo. XSEDE logo. NICS logo.



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NSF logo. NIMBioS is sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
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