BwUniCluster2.0/Software/R/Glmnet

Note that the instructions provided below refer to R 4.4.1 (but not R 4.2.1)!

General information

glmnet is an R package that fits generalized linear and similar models via penalized maximum likelihood, particularly, lasso and elastic-net.

Installation instructions

Preparations

Installing glmnet involves compiling source code. Therefore, ensure that the following flags are set in $HOME/.R/Makevars:

cat ~/.R/Makevars

CXX14=g++
CXX17=g++
CXX14FLAGS += -std=c++14
CXX17FLAGS += -std=c++17
CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option

If necessary, the appropriate compiler flags can be set by running the following lines of code:

mkdir -p ~/.R
echo "CXX14=g++" > ~/.R/Makevars
echo "CXX17=g++" >> ~/.R/Makevars
echo "CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option" >> ~/.R/Makevars
echo "CXX14FLAGS += -std=c++14" >> ~/.R/Makevars
echo "CXX17FLAGS += -std=c++17" >> ~/.R/Makevars

Since installing glmnet involves compiling, we start an interactive session on one of the compute nodes:

salloc -n 1 -t 30  -p dev_single

1. Load the R software module, e.g.

module load math/R/4.1.2

1. Prepare .R directory (if it does not already exists)

mkdir -p ~/.R

1. Write the following environment variables to Makevars
2. Skip the 2nd and 3rd commands below if Makevars already consist these variables (1st command shows content of Makevars)

cat ~/.R/Makevars echo "CXX17=icpc" >> ~/.R/Makevars echo "CXX17FLAGS=-O3 -fPIC -std=c++17 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp" >> ~/.R/Makevars

1. Install the glmnet package from within R session

R -q > install.packages("glmnet", dependencies=TRUE)

1. Run a quick test

> library(glmnet) > data(QuickStartExample) > x <- QuickStartExample$x > y <- QuickStartExample$y > fit <- glmnet(x, y) > print(fit)