Difference between revisions of "User:Ppomorsk"

Revision as of 12:27, 5 September 2014

Pawel Pomorski

High Performance Computing Programming Specialist

University of Waterloo

User:Ppomorsk/Workspace

Software which I help maintain

• CPMD

• CUDA

• GCC

• NAMD

• OpenCL

• Python

• SIESTA

• XCrySDen

• MKL

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NAMD instructions

These instructions for monk, current as of Feb.2014, with default modules:

intel/12.1.3            
cuda/5.5.22           

Preliminaries:

module unload openmpi
module load openmpi/intel/1.7.4
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/sharcnet/intel/12.1.3/icc/composer_xe_2011_sp1.9.293/compiler/lib/intel64

OpenMPI 1.7.4 is compiled with CUDA support. Environment variable is needed to locate all the libraries at runtime.

Get all the required source files:

tar xvfz NAMD_2.9_Source.tar.gz 
cd NAMD_2.9_Source

Download and install precompiled TCL and FFTW libraries: (working in NAMD source directory)

 wget http://www.ks.uiuc.edu/Research/namd/libraries/fftw-linux-x86_64.tar.gz
 tar xzf fftw-linux-x86_64.tar.gz
 mv linux-x86_64 fftw
 wget http://www.ks.uiuc.edu/Research/namd/libraries/tcl8.5.9-linux-x86_64.tar.gz
 wget http://www.ks.uiuc.edu/Research/namd/libraries/tcl8.5.9-linux-x86_64-threaded.tar.gz
 tar xzf tcl8.5.9-linux-x86_64.tar.gz
 tar xzf tcl8.5.9-linux-x86_64-threaded.tar.gz
 mv tcl8.5.9-linux-x86_64 tcl
 mv tcl8.5.9-linux-x86_64-threaded tcl-threaded

Compile charm, choosing which parallelization is to be used (threaded, MPI, MPI-SMP):

tar xvf charm-6.4.0.tar 
cd charm-6.4.0

Compile charm - MPI build:

env MPICXX=mpicxx ./build charm++ mpi-linux-x86_64  --no-build-shared --with-production

Compile charm - MPI-SMP build:

env MPICXX=mpicxx ./build charm++ mpi-linux-x86_64 smp --no-build-shared --with-production

Compile charm - threaded build:

./build charm++ multicore-linux64 --no-build-shared  icc8 --with-production

Configure NAMD and compile (with CUDA support enabled) - replace CHARM_ARCHITECTURE with one of the three options above:

./config  Linux-x86_64-icc   --charm-arch CHARM_ARCHITECTURE --with-cuda --cuda-prefix /opt/sharcnet/cuda/5.5.22/toolkit/
cd Linux-x86_64-icc
make

Test charm if needed:

From the directory charm is in,

cd architecture_directory/tests/charm++/megatest
make pgm
mpirun -n 4 ./pgm

Python installation instructions

Even though Python modules are provided by SHARCNET, sometimes there may be a need to compile your own Python and Numpy. Here are instructions on how to do it (tested in September, 2014):

To get the source tarballs, do:

wget --no-check-certificate https://www.python.org/ftp/python/2.7.8/Python-2.7.8.tgz
wget http://sourceforge.net/projects/numpy/files/NumPy/1.8.2/numpy-1.8.2.tar.gz

and unpack these somewhere in your directories.

These were built under modules:

module unload intel 
module unload mkl 
module unload openmpi 
module load gcc/4.8.2 
module load openmpi/gcc/1.8.1

(openmpi is not necessary for this built, but I would suggest loading the gcc/1.8.1 anyway)

Python was built with:

./configure --enable-shared --prefix=~/software_installs/python/2.7.8/gcc/installdir
make
make install

Then set ( in .bashrc for permanent change):

export PATH=~/software_installs/python/2.7.8/gcc/installdir/bin:$PATH
export LD_LIBRARY_PATH=~/software_installs/python/2.7.8/gcc/installdir/lib:$LD_LIBRARY_PATH

Numpy was built with:

unset LDFLAGS
python setup.py build --fcompiler=gnu95
python setup.py install --prefix=~/software_installs/numpy/1.8.2/gcc/installdir

Also did:

ln -sf ~/software_installs/numpy/1.8.2/gcc/installdir/lib/python2.7/site-packages/numpy/core/include/numpy ~/software_installs/python/2.7.8/gcc/installdir/include/python2.7

and finally

export PYTHONPATH=~/software_installs/numpy/1.8.2/gcc/installdir/lib/python2.7/site-packages/