NewSoftware

11/02/2016 - Siesta 4.1-b1

module load siesta

10/10/2016 - Jython 2.7.0

module load jython

Note: it is likely that the default python3 module will not allow the python2 module to load. To circumvent this problem, on Kong create a file ~/.modules with the following 2 lines :

module load sge
module load python2

09/28/2016 - lammps 30Jul16

module load lammps/[cpu,gpu]/30Jul16 

08/23/2016 - Ansys 17.1 for Linux 6,7

module load ansys/17.1 

08/09/2016 - FMRIB Software Library (FSL 5.0.9) for Linux 6,7

FSL is a comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data.

module load fsl/5.0.9

08/02/2016 - Open MPI for Scientific Linux 6,7

module load ompi/2.0.0

06/15/2016 - gcc 6.1.0 for Scientific Linux 6,7

module load gcc/6.1.0

06/15/2016 - gcc 5.4.0 for Scientific Linux 6,7

module load gcc/5.4.0

06/15/2016 - gcc 5.3.0 for Scientific Linux 6,7

module load gcc/5.3.0

05/13/2016 - Anaconda python 3.5.1

module load python3

05/13/2016 - Anaconda python 2.7.11

module load python2

03/18/2016 - R 3.2.4

module load R-Project

02/22/2016 - gcc 5.3.0 for Scientific Linux 7

module load gcc5/5.3.0-sl7

02/22/2016 - gcc 5.3.0 for Scientific Linux 6

module load gcc5

02/04/2016 - NWchem 6.6

module load nwchem

02/03/2016 - Gromacs 5.1.1

module load load gromacs/5.1.1

10/19/2015 - Intel Parallel Studio XE Cluster Edition

See "Intel" under "Compilers"

09/22/2015 - GNU gcc 5.2.0

GNU compilers. This installation does not include gfortran.

module load load gcc5

05/14/2015 - mafTools v01

Bioinformatics tools for dealing with Multiple Alignment Format (MAf)

module load load maftools

04/17/2015 - R 3.1.2

module load load R-Project

03/04/2015 - Freefem++

Freefem++ is a partial differential equation solver. It has its own language. freefem scripts can solve multiphysics non linear systems in 2D and 3D.

On stheno

module load freefem++

On kong and all SL6 AFS clients

module load freefem++-sl6

02/10/2015 - Anaconda Python

Enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing.

To use anaconda :

module load anaconda

Note that anaconda includes openmpi and the mpi4py python module.

Example python script using mpy4py

#!/afs/cad/linux/anaconda-2.1.0/anaconda/bin/python
from mpi4py import MPI

comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()

if rank == 0:
   data = [(x+1)**x for x in range(size)]
   print 'we will be scattering:',data
else:
   data = None
   
data = comm.scatter(data, root=0)
data += 1
print 'rank',rank,'has data:',data

newData = comm.gather(data,root=0)

if rank == 0:
   print 'master:',newData

Sample submit script for the mpi4py python script

#!/bin/sh 
# 
# EXAMPLE OMPI SCRIPT FOR SGE 
# Modified by Gwolosh from Basement Supercomputing 1/2/2006 DJE 
# To use, change "OMPI_JOB", "NUMBER_OF_CPUS",
# "OMPI_PROGRAM_NAME" and "UCID" to real values. 
# 
# Your job name 
#$ -N OMPI_JOB
# 
# Use current working directory 
#$ -cwd 
# 
# Join stdout and stderr 
#$ -j y 
# 
# pe request for OMPI. Set your number of processors here. 
# Make sure you use the "ompi" parallel environment. 
#$ -pe ompi NUMBER_OF_CPUS
# email beginning and end of job
#$ -m be 
#$ -M UCID@njit.edu 
# 
# Run job through bash shell 
#$ -S /bin/bash 
# 
#Generate an machine file
echo "Got $NSLOTS processors." 
echo $PE_HOSTFILE 
machines=$TMPDIR/machines 
touch $machines 
cat $PE_HOSTFILE | awk ' {print $1} ' >> $machines 
# For reporting only
cat $machines 
# 
# Use full pathname to make sure we are using the right mpiexec
/afs/cad/linux/anaconda-2.1.0/anaconda/bin/mpiexec -f $machines -np $NSLOTS /full/path/to/OMPI_PROGRAM_NAME