BwUniCluster2.0/Software/R/Rjags: Difference between revisions

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= Installation instructions: JAGS build using Intel Compiler & Intel MKL =


= General information =
Requirements: R, Intel compiler >= 17.0, Intel MKL >= 2017


rjags is a R interface to use JAGS, [https://mcmc-jags.sourceforge.io/ Just another Gibbs Sampler]. JAGS is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation
Consider starting an interactive job for compiling. Copy and paste the following to your shell.

rjags needs a JAGS installation on the side. We recommend to compile via Intel compiler and with the Intel MKL library (Intel Math Kernel Library), which allows JAGS to use various efficient implementations of mathematical computations. These are, as of now, loaded alongside with the module R 4.1.2.


= Installation =


<pre>
<pre>
# Load the R software module, e.g.
#Load R module
module load math/R/3.6.3
module load math/R/4.1.2

# Set up JAGS installation directory


# Check compiler and MKL version (see, requirements)
module list


# Set up JAGS installation directory
export JAGS_HOME=$HOME/sw/jags
export JAGS_HOME=$HOME/sw/jags


# Prepare JAGS source directory
# Prepare JAGS source directory (if not yet existing)

mkdir -p ~/src
mkdir -p ~/src
cd ~/src
cd ~/src


# Get JAGS
wget https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/JAGS-4.3.0.tar.gz
tar xf JAGS-4.3.0.tar.gz
cd JAGS-4.3.0


# Build JAGS
# Get JAGS source

wget https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/JAGS-4.3.1.tar.gz
tar xf JAGS-4.3.1.tar.gz
cd JAGS-4.3.1
rm JAGS-4.3.1.tar.gz




# Install JAGS
export CFLAGS="-O3 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp"
export CFLAGS="-O3 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp"
export CXXFLAGS="-O3 -std=c++14 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp"
export CXXFLAGS="-O3 -std=c++14 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp"
./configure --prefix=$JAGS_HOME --with-blas="-lmkl_rt -lpthread -lm"
./configure --prefix=$JAGS_HOME --with-blas="-lmkl_rt -lpthread -lm"
make -j8
make
make install
make install
cd
cd



# Set up environment
# Set up environment
export PKG_CONFIG_PATH=$JAGS_HOME/lib/pkgconfig
export PKG_CONFIG_PATH=$JAGS_HOME/lib/pkgconfig
export LD_RUN_PATH=$JAGS_HOME/lib
export LD_RUN_PATH=$JAGS_HOME/lib




# Install rjags package from within R session
# Install rjags package from within R session
R -q
R -q
install.packages("rjags", configure.args="--enable-rpath")
> install.packages("rjags", configure.args="--enable-rpath")
> library(rjags)
</pre>



----
[[Category:BwUniCluster]]
[[Category:BwUniCluster_2.0]]

Revision as of 12:04, 23 June 2022

General information

rjags is a R interface to use JAGS, Just another Gibbs Sampler. JAGS is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation

rjags needs a JAGS installation on the side. We recommend to compile via Intel compiler and with the Intel MKL library (Intel Math Kernel Library), which allows JAGS to use various efficient implementations of mathematical computations. These are, as of now, loaded alongside with the module R 4.1.2.


Installation

#Load R module
module load math/R/4.1.2

# Set up JAGS installation directory 


export JAGS_HOME=$HOME/sw/jags

# Prepare JAGS source directory (if not yet existing) 

mkdir -p ~/src
cd ~/src


# Get JAGS source

wget https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Source/JAGS-4.3.1.tar.gz
tar xf JAGS-4.3.1.tar.gz
cd JAGS-4.3.1
rm JAGS-4.3.1.tar.gz




# Install JAGS 
export CFLAGS="-O3 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp"
export CXXFLAGS="-O3  -std=c++14 -axCORE-AVX512,CORE-AVX2,AVX -xSSE4.2 -fp-model strict -qopenmp"
./configure  --prefix=$JAGS_HOME --with-blas="-lmkl_rt -lpthread -lm"
make
make install
cd


# Set up environment
export PKG_CONFIG_PATH=$JAGS_HOME/lib/pkgconfig
export LD_RUN_PATH=$JAGS_HOME/lib



# Install rjags package from within R session
R -q
> install.packages("rjags", configure.args="--enable-rpath")
> library(rjags)