R
Description
{{{name}}} website
R is a free software environment for statistical computing and graphics.
Template:App Location
Available versions
Note: File a support ticket to request installation of additional libraries.
- 2.14.1-mpi - R base package MPI-enabled via the Rmpi library.
- 2.14.2
- 2.15.0 (default)
Running the application using modules
To use R with the environment modules system at HPC the following commands are available:
Get module information for r:
$module spider R
Load the default application module:
$module load R
The modulefile for this software adds the directory with executable files to the shell execution PATH and sets the following environment variables:
- HPC_R_DIR - directory where R is located.
- HPC_R_BIN - executable directory
- HPC_R_LIB - library directory
- HPC_R_INCLUDE - includes directory
To use the version of R built for parallel execution with MPI via the Rmpi library load the following modules:
module load intel/11.1 openmpi/1.4.3 R
Installed Packages
Note: Many of the packages in the R library shown below are installed as a part of Bioconductor meta-library. The list is generated from the default R version.
affy Methods for Affymetrix Oligonucleotide Arrays
affydata Affymetrix Data for Demonstration Purpose
affyio Tools for parsing Affymetrix data files
affyPLM Methods for fitting probe-level models
affyQCReport QC Report Generation for affyBatch objects
akima Interpolation of irregularly spaced data
annaffy Annotation tools for Affymetrix biological
metadata
annotate Annotation for microarrays
AnnotationDbi Annotation Database Interface
ape Analyses of Phylogenetics and Evolution
base The R Base Package
baySeq Empirical Bayesian analysis of patterns of
differential expression in count data
Biobase Biobase: Base functions for Bioconductor
BiocGenerics Generic functions for Bioconductor
BiocInstaller Install/Update Bioconductor and CRAN Packages
Biostrings String objects representing biological
sequences, and matching algorithms
bitops Functions for Bitwise operations
boot Bootstrap Functions (originally by Angelo Canty
for S)
caTools Tools: moving window statistics, GIF, Base64,
ROC AUC, etc.
class Functions for Classification
cluster Cluster Analysis Extended Rousseeuw et al.
CNVtools A package to test genetic association with CNV
data
codetools Code Analysis Tools for R
colorspace Color Space Manipulation
compiler The R Compiler Package
datasets The R Datasets Package
DBI R Database Interface
DESeq Differential gene expression analysis based on
the negative binomial distribution
dichromat Color schemes for dichromats
digest Create cryptographic hash digests of R objects
doMC Foreach parallel adaptor for the multicore
package
doSNOW Foreach parallel adaptor for the snow package
DynDoc Dynamic document tools
edgeR Empirical analysis of digital gene expression
data in R
foreach Foreach looping construct for R
foreign Read Data Stored by Minitab, S, SAS, SPSS,
Stata, Systat, dBase, ...
gcrma Background Adjustment Using Sequence
Information
gdata Various R programming tools for data
manipulation
gee Generalized Estimation Equation solver
geiger Analysis of evolutionary diversification
genefilter genefilter: methods for filtering genes from
microarray experiments
geneplotter Graphics related functions for Bioconductor
GenomicRanges Representation and manipulation of genomic
intervals
ggplot2 An implementation of the Grammar of Graphics
glmmADMB Generalized Linear Mixed Models using AD Model
Builder
GO.db A set of annotation maps describing the entire
Gene Ontology
gplots Various R programming tools for plotting data
graphics The R Graphics Package
grDevices The R Graphics Devices and Support for Colours
and Fonts
grid The Grid Graphics Package
gtools Various R programming tools
hacks Convenient R Functions
hgu95av2.db Affymetrix Human Genome U95 Set annotation data
(chip hgu95av2)
HilbertVis Hilbert curve visualization
Hmisc Harrell Miscellaneous
IRanges Infrastructure for manipulating intervals on
sequences
iterators Iterator construct for R
itertools Iterator Tools
KEGG.db A set of annotation maps for KEGG
KernSmooth Functions for kernel smoothing for Wand & Jones
(1995)
labeling Axis Labeling
laser Likelihood Analysis of Speciation/Extinction
Rates from Phylogenies
lattice Lattice Graphics
leaps regression subset selection
limma Linear Models for Microarray Data
locfit Local Regression, Likelihood and Density
Estimation.
maanova Tools for analyzing Micro Array experiments
marray Exploratory analysis for two-color spotted
microarray data
MASS Support Functions and Datasets for Venables and
Ripley's MASS
Matrix Sparse and Dense Matrix Classes and Methods
memoise Memoise functions
methods Formal Methods and Classes
mgcv Mixed GAM Computation Vehicle with GCV/AIC/REML
smoothness estimation
msm Multi-state Markov and hidden Markov models in
continuous time
multicore Parallel processing of R code on machines with
multiple cores or CPUs
multtest Resampling-based multiple hypothesis testing
munsell Munsell colour system
mvtnorm Multivariate Normal and t Distributions
nlme Linear and Nonlinear Mixed Effects Models
nnet Feed-forward Neural Networks and Multinomial
Log-Linear Models
org.Hs.eg.db Genome wide annotation for Human
ouch Ornstein-Uhlenbeck models for phylogenetic
comparative hypotheses
parallel Support for Parallel computation in R
permute Functions for generating restricted
permutations of data
pheatmap Pretty Heatmaps
phylobase Base package for phylogenetic structures and
comparative data
picante R tools for integrating phylogenies and ecology
plyr Tools for splitting, applying and combining
data
preprocessCore A collection of pre-processing functions
prettyR Pretty descriptive stats.
proto Prototype object-based programming
qvalue Q-value estimation for false discovery rate
control
R2admb ADMB to R interface functions
RColorBrewer ColorBrewer palettes
Rcpp Seamless R and C++ Integration
reshape Flexibly reshape data.
reshape2 Flexibly reshape data: a reboot of the reshape
package.
rpart Recursive Partitioning
RSQLite SQLite interface for R
Rwave Time-Frequency analysis of 1-D signals
scales Scale functions for graphics.
simpleaffy Very simple high level analysis of Affymetrix
data
snow Simple Network of Workstations
spatial Functions for Kriging and Point Pattern
Analysis
splines Regression Spline Functions and Classes
statmod Statistical Modeling
stats The R Stats Package
stats4 Statistical Functions using S4 Classes
stringr Make it easier to work with strings.
subplex Subplex optimization algorithm
survival Survival analysis, including penalised
likelihood.
tcltk Tcl/Tk Interface
tools Tools for Package Development
utils The R Utils Package
vegan Community Ecology Package
vsn Variance stabilization and calibration for
microarray data
waveslim Basic wavelet routines for one-, two- and
three-dimensional signal processing
wavethresh Wavelets statistics and transforms.
XML Tools for parsing and generating XML within R
and S-Plus.
xtable Export tables to LaTeX or HTML
zlibbioc An R packaged zlib-1.2.5
FAQ
Q: When I submit the job with N=1 and M=1 it runs and R allocates the 10 slaves that I want. Is this the OK?
A: In short, no. This is bad since you are lying to the scheduler about the resources you intend to run. We have scripts that will kill your job if they catch it and we tend to suspend accounts of users who make a practice of it. :)
Q: The actual job I want to run is much larger. Anywhere from 31 to 93 processors are desired. Is it ok to request this many processors.
A: That depends on the level of investment from your PI. If you ask for processors than your groups core allocation, which depends on the investment level, you will be essentially borrowing cores from other groups and may wait an extended period of time in the queue before your job runs. Groups are allowed to run on up to 10x their core allocation provided the resources are available. If you ask for more than 10x your groups core allocation, the job will be blocked indefinitely.
Q: Do I need the number of nodes requested to be correct or can I just have R go grab slaves after the job is submitted with N=1 and M=1?
A: Your resource request must be consistent with what you actually intend to use as noted above.
Q: Is it better to request a large number of nodes for a shorter period of time or less nodes for longer period of time (concretely, say 8 nodes for 40 hours versus 16 nodes for 20 hours) in terms of getting through the queue?
A: Do not confuse "nodes" with "cores/processors". Each "node" is a physical machine with between 4 and 48 cores. Your MPI threads will run on "cores" which may all be in the same "node" or spread among multiple nodes. You should ask for the number of cores you need and spread them among as few nodes as possible unless you have a good reason to do otherwise. Thus you should generally ask for things like
#PBS -l nodes=1:ppn=8 (we have lots of 8p nodes) #PBS -l nodes=1:ppn=12 (we have a number of 12p also)
Multiples of the above work as well so you might ask for nodes=3:ppn=8 if you want to run 24 threads on 24 different cores.
It looks like in the R model there is a master/slave paradigm so you really need one master thread to manage the "slave" threads. It is likely that the master thread accumulates little CPU time so you could neglect it. In other words tell the scheduler that you want nodes=3:ppn=8 and tell R to spawn 24 children. This is a white lie which will do little harm. However, if it turns out that the master accumulates significant CPU time and your job gets killed by our rogue process killer, you can ask for the resources as follows
#PBS -l nodes=1:ppn=1infiniband+3:ppn=8:infiniband
This will allocate 1 thread on a separate node (the master thread) and then the slave threads will be allocated on 3 additional nodes with at least 8 cores each.
Example of using the parallel module to run MPI jobs under R 2.14.1+
{{#fileAnchor: rmpi_test.R}} Download raw source of the [{{#fileLink: rmpi_test.R}} rmpi_test.R] file.
# Load the R MPI package if it is not already loaded.
if (!is.loaded("mpi_initialize")) {
library("Rmpi")
}
# Spawn as many slaves as possible
mpi.spawn.Rslaves()
# In case R exits unexpectedly, have it automatically clean up
# resources taken up by Rmpi (slaves, memory, etc...)
.Last <- function(){
if (is.loaded("mpi_initialize")){
if (mpi.comm.size(1) > 0){
print("Please use mpi.close.Rslaves() to close slaves.")
mpi.close.Rslaves()
}
print("Please use mpi.quit() to quit R")
.Call("mpi_finalize")
}
}
# Tell all slaves to return a message identifying themselves
mpi.remote.exec(paste("I am",mpi.comm.rank(),"of",mpi.comm.size()))
# Tell all slaves to close down, and exit the program
mpi.close.Rslaves()
mpi.quit()