Oscar

Oscar is Brown's high-performance computing cluster managed by the Brown Center for Computation and Visualization (CCV). Long-term visitors to ICERM with a Brown sponsored ID are provided with an exploratory account on Oscar upon arrival. Oscar access may also be requested by short-term visitors on a request-basis with advance notice.

Overview of Oscar
Open OnDemand Access to Oscar
SSH Access to Oscar
Oscar Software Modules

Currently Available Modules
Oscar: Sage
Oscar: MATLAB
Oscar: Mathematica

Overview of Oscar

Long-term visitors to ICERM who receive a Brown ID are provided with an exploratory account on the Oscar high performance computing cluster maintained by Brown's Center for Computation and Visualization. Oscar access may also be provided to short-term visitors on request-basis with advance notice.

If you are coming for a short-term visit and would like to request Oscar access, please contact ICERM IT staff no later than three weeks before your planned visit to ensure enough time to process a sponsored ID and enable Oscar access.

Oscar can be used via SSH terminal or via the Oscar VNC client. The Oscar VNC client is preinstalled and available on the thin clients in all ICERM 10th floor offices and the graduate student workspace in the 10th floor lobby.

Using Oscar

Oscar provides both command line (SSH) access and virtual desktop (VNC) access to the computing cluster. All Oscar accounts are capable of using both interfaces, so you should choose the method that will work best for what you are trying to accomplish.

Oscar accounts are tied to Brown Shibboleth accounts, so you must have an ICERM Sponsored Brown ID to use Oscar. Once Oscar access is enabled for your Brown account, you can log in to SSH and VNC using your Brown Shibboleth username and password.

SSH Usage

Oscar's SSH interface provides a standard command line shell for interacting with the cluster. This is the best option for using simple command line scripts and submitting scripts for batch job processing. See the SSH Login Instructions for information on how to connect and basic usage. It's important to note that you should never run complex scripts or computations within the SSH login nodes. Computations should either be submitted as batch jobs or run in an interactive compute session using the interact command.

Open OnDemand

Oscar's OOD s a web portal to the Oscar computing cluster. This is the easiest way to access Oscar and navigate around. Once logged in you effortlessly be able to access active jobs, use MATLAB or Mathematica, and even use the Oscar Shell CLI. See the OOD Login Instructions for information on how to get started with the web portal.

VNC Usage

Oscar's VNC client provides a standard Linux graphical desktop using virtual network computing. If you'd like to use applications with a GUI, such as MATLAB or Mathematica, this is the best way to do so. Oscar's VNC sessions run within the scheduling system directly on a compute node, similar to the interact command, so you can run computations directly in the VNC session. See the VNC Login Instructions for information on how to get started with the VNC client.

Available Software

Oscar maintains a large library of software packages for use on the HPC cluster. Some of the most commonly used mathematical applications and languages available include:

MATLAB
Mathematica
Sage
Julia
R
Python
Maple
Magma

To see the most up to date list of available software, log in to your Oscar account and run the terminal command module avail.

Loading Software Modules

Oscar has a large library of software available on the cluster, but only a few apps are pre-loaded in to your sessions. The commands below will allow you to list all available modules, search the list of modules, and load/unload software packages. When using Oscar over VNC, you must open the Terminal Emulator to run these commands.

To view all available software packages, type module avail.
To search the list of available packages, type module avail <package>.
For example, to search for all available versions of mathematica: module avail mathematica. Many packages, like Mathematica, have multiple versions available. This command lets you see all available versions of the package you searched for.
To load a package into your session, type module load <package/version>.
For example, module load mathematica/11.0. This will load the Mathematica 11.0 into your session and make it available for use.
To unload a package you are no longer using, type module unload <packagename>

Module-specific Guides

Running Jobs

Oscar supports two main methods of running jobs: batch jobs and interactive sessions.

Batch jobs are pre-scripted and can be submitted to the cluster's scheduler via the sbatch command.
Interactive jobs are command-line sessions run directly on a compute node via the interact command that can be used in real time.

The Running Jobs page of the Oscar User Manual provides the most detailed and up-to-date instructions on scripting and submitting jobs.

Important Notes About the Oscar Cluster

Please do not run any computations or simulations on the login nodes, as they are shared with other users. Use SLURM to submit a batch job to the queue for computations or start an interactive session on one of the compute nodes with the command interact.
The full Oscar User Manual is available on the CCV website.
Oscar uses SLURM for managing batch jobs and interactive sessions on the cluster. Detailed instructions on submitting jobs is available on the Running Jobs page of the Oscar User Manual.
CCV has a large library of software already installed on the cluster. For a full list of available software, run the command module avail. More information about Oscar's software packages are available in the Oscar User Manual. If you require a software package that is not currently available on the Oscar cluster, please contact ICERM's IT staff and we will work with CCV to get the software installed.
Users can install sub-packages for some modules (like Sage and Python) to their home folders on their own by using the –user as an option in the install command. For example, with Sage, the command would be something like sage -i <package name> –user.
The Oscar VNC Client is a Java application that can be installed on most laptops with Java installed. If you wish to install the client on your own laptop, please make sure to download the latest version of CCV VNC Client 2.0+ to ensure proper support and compatibility.

If you have questions about these instructions or require further assistance, please contact the ICERM IT staff by dropping by the administrative offices or emailing support@icerm.brown.edu.

Open OnDemand Access to Oscar

Open OnDemand (OOD) is a web portal to the Oscar computing cluster. An Oscar account is required to access Open OnDemand. Visit this link in a web browser and sign in with your Brown username and password to access this portal.

Benefits of OOD:

No installation needed. Just use your favorite browser!
No need to enter your password again. SSH into Oscar in seconds!
No need to use two-factor authentication multiple times. Just do it once, when you log into OOD.
Use it with, or without, VPN. Your workflow remains the same.
Just a good time over all 🥳

Once logged in you'll see a landing page that looks similar to the photo below:

Screen Shot 2022-10-26 at 13.57.44.png

Guides to help you figure your way out once logged in:

Using File Explorer on OOD

Web-based Terminal App

Interactive Apps on OOD

Using Python or Conda environments in the Jupyter App

SSH Access to Oscar

1. Open a terminal prompt. On the ICERM thin clients, enter the username user and the password icerm to log in to the terminal.

2. In the terminal, type ssh <your ccv username>@ssh.ccv.brown.edu. If you are asked to verify the authenticity of the host 'ssh.ccv.brown.edu', type yes.

3. You will now be prompted for your password. Enter your password (nothing will show up when you type in the terminal password prompt) and press enter.

4. Once logged in, you should see a “Welcome to Oscar!” message. This means you're now connected to one of the login nodes, which you can use to manage your files and submit batch jobs.

Please DO NOT run any computations directly on the login nodes. Use the batch system to submit your computations to the queue to be processed on the computation nodes or start an interactive session on one of the compute nodes with the command interact. See CCV's Oscar Documentation page on Running Jobs for detailed instructions on batch jobs and interactive sessions.

Oscar Software Modules

List and instructions for software modules available on the Oscar cluster.

Oscar Software Modules

Currently Available Modules

If you require a software package that is not currently available on the Oscar cluster, please contact ICERM's IT staff and we will work with CCV to get the software installed.

This list is current as of November 10, 2021. To see the most up to date list of software modules, log into your Oscar account and run the command module avail.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bxh_xcede_tools/1.11.14  confocal/1.0             potfit/20201014
cesm_post_process/Oct18  pigz/2.4
~~~~~~~~~~~~~~~~~~~~~~ category:  Microstructure modeling ~~~~~~~~~~~~~~~~~~~~~~
oof2/2.1.17
~~~~~~~~~~~~~~~~~~~~~~~~~ category:  access controlled ~~~~~~~~~~~~~~~~~~~~~~~~~
Molpro/2012.1.15
Molpro/2015_gcc
Molpro/2015_serial
Molpro/2018.2_ga
Molpro/2019.2
Molpro/2019.2_ga
Molpro/2020.1
Molpro/2020.1_ga
Molpro/2020.1_openmpi_4.0.5_gcc_10.2_slurm20
gaussian/g09
gaussian/g09-D01
gaussian/g09-D01-TEST
vasp/5.4.1
vasp/5.4.1_debug
vasp/5.4.1_mvapich2-2.3.5_intel_2020.2_slurm20
vasp/5.4.4
vasp/5.4.4_intel
vasp/5.4.4_mvapich2-2.3.5_intel_2020.2_slurm20
vasp/5.4.4_openmpi_4.0.5_gcc_10.2_slurm20
vasp/5.4.4a
vasp/6.1.1_ompi405_yqi27
vasp/6.1.1_openmpi_4.0.5_intel_2020.2_yqi27_slurm20
vasp/6.1.1_yqi27
~~~~~~~~~~~~~~~~~~~~~~~~ category:  graph partitioning ~~~~~~~~~~~~~~~~~~~~~~~~~
metis/5.1.0     parmetis/4.0.3  scotch/6.0.4
~~~~~~~~~~~~~~~~~~~~~~~~~ category:  image processing ~~~~~~~~~~~~~~~~~~~~~~~~~~
multinest/3.10   openslide/3.4.1  pstokes/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
astropy/3.2.1     cudnn/7.4         flann/1.8.4       lapack/3.7.0
astroquery/3.0.9  cudnn/7.6         hnn/1.0           mumps/5.0.2
blas/3.7.0        cudnn/7.6.5       horovod/0.16      mumps/5.0.2-seq
cudnn/5.1         cudnn/8.1.0       horovod/0.19.5    pcl/1.9.1
cudnn/6.0         cudnn/8.2.0       lapack/3.4.2      pcl/1.9.1_nurbs
cudnn/7.0         dlib/19.17        lapack/3.6.0      scalapack/2.0.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  library ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
openslide-python/1.1.1
~~~~~~~~~~~~~~~~~~~~~~~~~ category:  machine learning ~~~~~~~~~~~~~~~~~~~~~~~~~~
keras/2.0.9      keras/2.1.3_py3  nccl/2.8.4
keras/2.1.1      nccl/2.4.7
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  math ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
gsl/1.15  gsl/2.3   gsl/2.5
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  package ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
subread/1.6.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  software ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
mriconvert/2.1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  utility ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
mriconvert/2.1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~ category:  visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~
mayavi/4.6.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: (none) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
HiC-Pro/2.7.8            jo/1.4                   repeatmasker/4.1.0
HiC-Pro~/2.10.0          libsodium/1.0.17         repeatmodeler/1.0.11
awscli/1.0               mumax/3.9.3              sdl2/2.0.12
basilisk/1.0             ncl/6.4.0                siesta/3.2
canu/1.7.1               neuron/7.5               siesta/4.1
dropest/0.86             neuron/7.7               skewer/0.2.1
fiji/2017-java6          neuron/7.7_mpi           squashfs/4.3
fiji/2020                nvidia-driver/440.33.01  tmux/2.8
fmri/20.0.0              pdflib/7.0.5             topcat/4.7
fv/5.5                   pypy/6.0.0_2.7           transrate/1.0.3
gcm-core/2.0.498         pypy/6.0.0_3.5           vscode/1.22
grads/2.2.0              pypy/7.3.0_3.6           zstd/1.5.0
hotnet/1.0               repeatmasker/4.0.7
~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Applied Maths ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
oof2/2.1.17
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Astrophysics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
polychord/1.0  polychord/2.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Bioinformatics ~~~~~~~~~~~~~~~~~~~~~~~~~~~
MultiQC/1.0    muscle/3.8.31
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: CAD ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
freecad/0.18  librecad/2.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Chemistry ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dalton/2018.0
dalton/2018.0_mvapich2-2.3.5_intel_2020.2_slurm20
~~~~~~~~~~~~~~~~~~~~~~~ category: Computer Architecture ~~~~~~~~~~~~~~~~~~~~~~~~
risc/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: EDA tools ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cadence/IC06.18.090  cadence/IC6.1.8.33   incisive/15.20.085
~~~~~~~~~~~~~~~~~~~~ category: Executable for ceckpointings ~~~~~~~~~~~~~~~~~~~~
dmtcp/2.6.0
~~~~~~~~~~~~~~~~~~~ category: Flag for CMAKE for fast build ~~~~~~~~~~~~~~~~~~~~
ninja/1.9.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: GENETEICS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
eigensoft/6.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Genomic ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Genrich/0.5  mcscanx/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Genomic Library ~~~~~~~~~~~~~~~~~~~~~~~~~~~
redundans/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Genomics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
baypass/2.2      beast/2.5.2      kissplice/2.5.1
beast/1.10.4     kissplice/2.5.0
~~~~~~~~~~~~~~~~~~~ category: Genomics Sequencing Alignment ~~~~~~~~~~~~~~~~~~~~
qualimap/2.2.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Graphics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
beagle/1.0.0
~~~~~~~~~~~~~~~~~~~~~~~~ category: Helper function type ~~~~~~~~~~~~~~~~~~~~~~~~
datamash/1.3
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: I/O ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cdo/1.9.8
cdo/1.9.9
hdf5/1.10.0
hdf5/1.10.1_mvapich2-2.3.5_gcc_10.2_slurm20
hdf5/1.10.1_parallel
hdf5/1.10.5
hdf5/1.10.5_fortran
hdf5/1.10.5_mvapich2-2.3.5_intel_2020.2_slurm20
hdf5/1.10.5_openmpi_3.1.3_gcc
hdf5/1.10.5_openmpi_3.1.6_gcc
hdf5/1.10.5_openmpi_4.0.0_gcc
hdf5/1.10.5_openmpi_4.0.5_gcc_10.2_slurm20
hdf5/1.10.5_parallel
hdf5/1.10.7_hpcx_2.7.0_intel_2020.2_slurm20
hdf5/1.10.7_openmpi_4.0.5_gcc_10.2_slurm20
hdf5/1.10.7_openmpi_4.0.5_intel_2020.2_slurm20
hdf5/1.12.0_hpcx_2.7.0_intel_2020.2
hdf5/1.12.0_hpcx_2.7.0_intel_2020.2_slurm20
hdf5/1.12.0_openmpi_4.0.5_intel_2020.2_slurm20
jasper/1.900.1-intel
nco/4.6.6
nco/4.8.2
nco/4.9.3
netcdf/3.6.3
netcdf/4.4.1.1_gcc
netcdf/4.4.1.1_intel
netcdf/4.4.1.1_pgi
netcdf/4.7.0_intel2019.3
netcdf/4.7.4_gcc8.3
netcdf/4.7.4_gcc_10.2_hdf5_1.10.5
netcdf/4.7.4_intel_2020.2_hdf5_1.12.0
sqlite/3.25.2
sqlite/3.31.1
udunits/1.12.11
udunits/2.2.24
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: IDE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
meshlab/20190129_qt59  spyder/3.3.5
~~~~~~~~~~~~~~~~~ category: Image processing and visualization ~~~~~~~~~~~~~~~~~
opencv-python/4.1.0.25
~~~~~~~~~~~~~~~~~~~~~~~ category: Library for optimizer ~~~~~~~~~~~~~~~~~~~~~~~~
numbbo/2.3_python2  numbbo/2.3_python3
~ category: Material sceince: https://icet.materialsmodeling.org/overview.html ~
icet/0.3
~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Medical imaging ~~~~~~~~~~~~~~~~~~~~~~~~~~~
mricrogl/1.0           mricrogl/1.2.20210317
~~~~~~~~~~~~~~~ category: Multifunctional Wavefunction Analyzer ~~~~~~~~~~~~~~~~
multiwfn/3.3.9
~~~~~~~~~~~~~~~~~~~~~~~~~ category: Numerical Library ~~~~~~~~~~~~~~~~~~~~~~~~~~
ngsolve/6.2.1901
~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Numerical Solver ~~~~~~~~~~~~~~~~~~~~~~~~~~
dedalus/2.1810
dedalus/2.1905
dedalus/2.1905_openmpi_4.05_gcc_10.2_slurm20
dedalus/Sep2019
nektar++/4.5.0
~~~~~~~~~~~~~~~~~~~~~~~~ category: Quantum Monte Carlo ~~~~~~~~~~~~~~~~~~~~~~~~~
casino/2.13
~~~~~~~~~~~~~~~~~~~~ category: Tool for Image Manipulation ~~~~~~~~~~~~~~~~~~~~~
netpbm/10.47.71
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: Utility ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
globus/1.11
~~~~~~~~~~~~~~~ category: Visualization Package Qt's Python API ~~~~~~~~~~~~~~~~
pyqt/4.12.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: X ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
turbovnc/2.1.1   turbovnc/2.2.1   turbovnc/2.2.6   turbovnc/2.2.b1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: astronomy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ares/0.1                iraf/2.16               sextractor/2.8.6
ares/0.5                iraf/2.16.1+2018.11.01  supermongo/2.4.34
ccfits/2.5              lsst/14.0               swarp/2.38
cfitsio/3.450           planck_likelihood/3.0   theli/1.9.5
cfitsio/3.48            planck_likelihood/3.01  wcstools/3.9.5
ds9/7.6                 scamp/2.0.4
galfit/3.0.5            sextractor/2.19.5
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: astrophysics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
fiat/Oct2019
~~~~~~~~~~~~~~~~~~~~~~~~~ category: astropy computing ~~~~~~~~~~~~~~~~~~~~~~~~~~
astropy/3.2.1
~~~~~~~~~~~~~~~~~~~~~~~~ category: astroquery computing ~~~~~~~~~~~~~~~~~~~~~~~~
astroquery/3.0.9
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: benchmark ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
elbencho/2.0-1
~~~~~~~~~~~~~~~~~~~~~ category: binary for genome studies ~~~~~~~~~~~~~~~~~~~~~~
bedGraphToBigWig/1.04
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: bio ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
SNAP/2013-11-29                         mira/4.0.2
abyss/2.1.1                             mne/0.17.0
ale/20140120                            moseq2/0.1.2
aliview/1.25                            moseq2/0.2.0
amplisat/20170208                       mothur/1.39.5
angsd/0.920                             mrbayes/3.2.6
anvio/5.2                               mriqc/0.15.2
anvio/5.5                               msmc/1.0.0
anvio/6.1                               msmc/1.1.0
anvio/7                                 msmc/2.1.2
arb/6.0.6                               msprime/0.7.0
augustus/3.3                            msprime/0.7.2
bamcleave/Oct2018                       msprime/0.7.4
bamtools/2.3.0                          mummer/4.0.0.beta2
bamtools/2.4.1                          namd/2.11-multicore
bbmap/38.23                             namd/2.13b1-multicore
bbtools/38.12                           nda-tools/1.0
bcftools/1.10.2                         nextclip/1.3.1
bcftools/1.13                           nextgenmap/0.5.2
bcftools/1.9                            nipype/1.5.0
bcl2fastq/2.20.0                        nseg/20181012
bedops/2.4.35                           nvtop/1.1.0
bedtools/2.25.0                         obitools/1.2.12
bedtools/2.26.0                         octopus/0.7.4
beeline/1.0                             oligotyping/2.0
biolite/1.0.0                           omero/5.6.2
biomed/1.0                              opera/2.0.6
biopython/1.66                          orthomcl/2.0.9
biopython/1.73                          paml/4.8
bismark/0.20.0                          pandaseq/2.11
blast/2.2.30+                           paris/1.1.3
blast/2.6.0+                            paup/4.0a157
blast/2.7.1+                            paup/4.0a166
blast/2.8.1+                            paup/4.0a168
blast/2.9.0+                            pbsuite/15.8.24
blat/36x2                               pcangsd/0.98
bowtie/1.2.0                            picard-tools/2.17.11
bowtie/2.4.1                            picard-tools/2.9.2
bowtie2/2.3.0                           pilon/1.22
bowtie2/2.3.5.1                         pilon/1.24
bowtie2/2.4.2                           plink/1.07
braker/2.1.0                            plink/1.90
busco/3.0.2                             plink/2.00
bwa/0.7.15                              polyphen/2.2.2
cabana/1.0                              poolparty/0.8
cabana/1.1_hpcx_2.7.0_gcc_10.2_slurm20  popoolation2/1.201
cantera/5.9.7                           prinseq/0.20.4
ccp4/7.0                                prodigal/2.6.3
circlator/1.5.5                         psmc/0.6.5
conn/16b                                psmc/0.6.5a
conn/18a                                pymol/2.2b
conn/18b                                qctool/2.0
conn/18b_runtime                        qiime/1.9.1
conn/18b_standalone                     quast/5.0.0
conn/19c                                raremetal/4.14.1
conn/20b                                raxml/8.2.10
conn/20b_standalone                     raxml-ng/0.9.0
connectome_workbench/1.3.1              recon/1.08
connectome_workbench/1.3.2              repeatscout/1.0.5
connectome_workbench/1.4.2              revbayes/1.0.5
cpac/1.0.1                              revbayes/1.0.6
cpac/1.5.0                              rmblastn/2.10.0
cufflinks/2.2.1                         rmblastn/2.6.0
cutadapt/1.14                           rsem/1.3.1
dada2/1.4.1                             salmon/0.11.3
dada2/1.8                               salmon/0.8.2
dbg2olc/Sep2018                         salmon/1.0.0
dendropy/4.2.0                          salmon/1.2.0
diamond/0.9.12                          salmon/1.3.0
diamond/0.9.24                          samblaster/0.1.24
diamond/2.0.8                           samtools/0.1.18
dida/1.0.1                              samtools/1.10
diffreps/1.55.6                         samtools/1.12
dlcpar/1.1                              samtools/1.13
edge-pro/1.3.1                          samtools/1.2
eems/1.0                                samtools/1.3.1
emboss/6.6.0                            samtools/1.4.1
entap/0.8.1-beta                        samtools/1.9
entap/0.8.4-beta                        schrodinger/2017-3
exonerate/2.2.0                         schrodinger/2020-1
fastme/2.1.5                            schrodinger/2020-3
fastqc/0.11.5                           schrodinger/2021-1
fastsimcoal/2                           schrodinger/2021-3
fasttree/2.1.10                         selscan/1.2.0a
fasttree/2.1.11                         seqkit/0.10.1
fastuniq/1.1                            seqtk/1.3
fastx-toolkit/2.6                       sga/0.10.15
fgap/1.8.1                              sift/6.2.1
fgbio/0.6.1                             sift4g/2.0.0
figtree/1.4.4                           sjaracne/0.2.0
flashpca/2.0                            skesa/1.68
freebayes/1.1.0                         slicer/4.10.0
freebayes/1.2.0                         slim/3.1
garlic/1.1.6                            slim/3.2.1
gatk/4.0.9.0                            slim/3.3
gatk/4.1.6.0                            snippy/Oct2017
gatk/4.2.2.0                            spades/3.10.1
gdcclient/1.4.0                         spades/3.13.0
genemark/4.33                           span/0.10.0.4787
guppy/4.0.11                            spm/spm12
guppy/4.2.2                             spm/spm12_7771
guppy/5.0.7                             sprint/0.1.8
hapbin/1.3.0                            sratoolkit/2.10.5
hisat2/2.1.0                            sratoolkit/2.11.0
hmmer/3.1b2                             sratoolkit/2.8.2-1
homer/4.10                              stacks/2.2
hotnet2/1.2.1                           stampy/1.0.32
htseq/0.11.1                            star/2.6.1b
htseq/0.9.1                             star/2.7.2a
idba/1.1.3                              star/2.7.3a
idemp/20180928                          starfusion/1.7.0
ilash/1.0.0                             starfusion/1.8.1
ilash/1.0.2                             stringtie/1.3.3b
interproscan/5.26-65.0                  stringtie/1.3.4d
interproscan/5.27-66.0                  tabix/0.2.6
interproscan/5.28-67.0                  tablemaker/2.1.1
interproscan/5.32-71.0                  taxonkit/0.3.0
interproscan/5.33-72.0                  tophat/2.1.1
iq-tree/1.6.7                           transabyss/2.0.1
iq-tree/2.0.6                           transindel/20181017
jellyfish/1.1.11                        trf/4.09
jellyfish/2.2.10                        trimal/1.4
kneaddata/0.6.1                         trimgalore/0.5.0
kraken/2.0.7-beta                       trimmomatic/0.36
kraken/2.0.9-beta                       trinity/2.11.0
kraken/2.1.2                            trinity/2.4.0
last/956                                trinity/2.8.4
macs/2.1.1                              usearch/5.2.32
macs/2.2.6                              usearch/8.1.1861
mafft/7.310-with-extensions             varscan/2.3.9
maker/2.31.10                           vcftools/0.1.16
marsbar/0.42                            velvet/1.2.10
maxbin2/2.2.5                           velvet/1.2.10a
megahit/1.1.3                           vep/101
meraculous/2.2.6                        vep/104
metabat/2.12.1                          viennarna/2.4.9
metaphlan/2.6.0                         vmd/1.9.3
metavelvet/1.2.01                       vsearch/2.11.1
migrate/3.6.11                          weblogo/2.8.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: bio packages ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bwameth/0.10
~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: bioinformatics ~~~~~~~~~~~~~~~~~~~~~~~~~~~
umitools/1.0.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: biology ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
haplostrips/1.2.1  htslib/1.9         meme/5.0.5         mmsplice/0.2.7
htslib/1.13        libBigWig/0.4.4    meme/5.3.0         wiggletools/1.2.3
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: build tool ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
scons/3.0.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: cave ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
gigapixelviewer/alpha  minvr/dev_ben          vrg3d/265_compat
minvr/0.3master        planetaryviewer/alpha  vrg3d/265_demos
minvr/0.4              scalable/20150828      vrpn/7.33
minvr/beta             scalable/opengl32+
minvr/beta-bleeding    vrg3d/265
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: chemistry ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Molpro/2012.1.15
Molpro/2015_gcc
Molpro/2015_serial
Molpro/2018.2_ga
Molpro/2019.2
Molpro/2019.2_ga
Molpro/2020.1
Molpro/2020.1_ga
Molpro/2020.1_openmpi_4.0.5_gcc_10.2_slurm20
ambertools/amber16
ambertools/amber16-gpu
ambertools/amber17
ambertools/amber17_lic
ambertools/amber21
ase/3.13.0
ase/3.19.1
ase/3.8.1
bagel/1.2.2
cp2k/7.1
cp2k/8.1.0
crossrate/2016.3.23
dacapo/2.7.16_mvapich2_intel
dice/1.0
elk/5.2.14
gaussian/g09
gaussian/g09-D01
gaussian/g09-D01-TEST
gaussview/14Aug20
gromacs/2016.6
gromacs/2018.2
gromacs/2018.2_gpu
gromacs/2018.2_hpcx_2.7.0_gcc_10.2_slurm20
gromacs/2020.1
gromacs/2020.1_hpcx_2.7.0_gcc_10.2_slurm20
gromacs/2020.4_gpu
gromacs/2020.4_gpu_hpcx_2.7.0_gcc_10.2_slurm20
gromacs/2020.4_hpcx_2.7.0_gcc_10.2_slurm20
hande/1.1.1
hande/1.1.1_64
hande/1.1.1_debug
hotbit/10mar17
lammps/11Aug17_serial
lammps/29Oct20_openmpi_4.0.5_gcc_10.2_slurm20
medea/3.2.3.0
molden/5.7
mrcc/2014-intel
mrcc/2014-intel-threaded
mrcc/2018-intel
mrcc/2018-intel-threaded
mrcc/2020
n2p2/1.0.0
n2p2/2.0.0
n2p2/2.0.0_hpcx
nbo/7.0
nwchem/6.8-openmpi
nwchem/7.0
nwchem/7.0.2_mvapich2-2.3.5_intel_2020.2_slurm20
openbabel/2.4.1
openbabel/3.0.0
openmolcas/18.09.617
orca/4.0.1.2
orca/4.1.1
orca/4.2.1
orca/5.0.0
orca/5.0.1
prophet/augustegm_1.2
pyscf/1.4.7
pyscf/1.6.3
qchem/5.0.2
qchem/5.0.2-openmpi
qmcpack/3.10.0_openmpi_4.0.5_intel_2020.2_slurm20
qmcpack/3.7.0
qmcpack/3.9.1
qmcpack/3.9.1_openmpi_3.1.6
qmcpack/3.9.2_hpcx_2.7.0_gcc_10.2_slurm20
qmcpack/3.9.2_intel_2020
qmcpack/3.9.2_openmpi_4.0.0_gcc_8.3_slurm20
qmcpack/3.9.2_openmpi_4.0.0_gcc_8.3_slurm20_complex
qmcpack/3.9.2_openmpi_4.0.1_gcc
qmcpack/3.9.2_openmpi_4.0.4_gcc
qmcpack/3.9.2_openmpi_4.0.5_intel_2020.2_slurm20
quantumespresso/6.1
quantumespresso/6.3
quantumespresso/6.4
quantumespresso/6.4.1
quantumespresso/6.4_openmpi_4.0.0_gcc_8.3_slurm20
quantumespresso/6.4_openmpi_4.0.5_intel_2020.2_slurm20
quantumespresso/6.4_openmpi_4.0.5_intel_slurm20
quantumespresso/6.5
quantumespresso/6.5_openmpi_4.0.5_intel_slurm20
quantumespresso/6.6
quantumespresso/6.6_openmpi_4.0.5_intel_2020.2_slurm20
rotd/2014-11-15_mvapich2
schrodinger/2017-3
schrodinger/2020-1
schrodinger/2020-3
schrodinger/2021-1
schrodinger/2021-3
sharc/2.0
sharc/2.1.1
sharc/2.1.1_intel
vasp/5.4.1
vasp/5.4.1_debug
vasp/5.4.1_mvapich2-2.3.5_intel_2020.2_slurm20
vasp/5.4.4
vasp/5.4.4_intel
vasp/5.4.4_mvapich2-2.3.5_intel_2020.2_slurm20
vasp/5.4.4_openmpi_4.0.5_gcc_10.2_slurm20
vasp/5.4.4a
vasp/6.1.1_ompi405_yqi27
vasp/6.1.1_openmpi_4.0.5_intel_2020.2_yqi27_slurm20
vasp/6.1.1_yqi27
xcrysden/1.5.60
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: class ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class/1435
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: clients ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
aspera/3.8.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: climate ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cesm/1.2.1     cesm/2.1.1     esmf/8.0.0     esmf/8.1.0b11
cesm/1.2.2     esmf/7.1.0r    esmf/8.0.0b    esmf/8.1.9b17
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: compilers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Xvfb/1.20.12            gcc/10.2                java/8u111
clang/3.9.1             gcc/4.5.4               java/jdk-11.0.11
clang/7.0.0             gcc/4.7.2               java/jdk-12.0.2
clang/7.1.0             gcc/4.9.4               kokkos/3.3.1
cuda/10.0.130           gcc/5.4                 kokkos/3.4.1
cuda/10.1.105           gcc/6.2                 llvm/11.0.1
cuda/10.2               gcc/6.3                 llvm/3.8.1
cuda/11.1.1             gcc/7.2                 llvm/4.0.0
cuda/11.1.1_intel_2020  gcc/8.3                 llvm/6.0.1
cuda/11.3.1             intel/2011.11.339       llvm/7.1.0
cuda/7.5.18             intel/2013.1.106        nag/6.2
cuda/8.0.61             intel/2017.0            nag/mbl6i25dnl
cuda/9.0.176            intel/2018.1            pgi/16.7
cuda/9.1.85.1           intel/2019.3            pgi/2019
cuda/9.2.148            intel/2020.2            swig/3.0.10
~~~~~~~~~~~~~~~~~~~~~~~ category: computer architecture ~~~~~~~~~~~~~~~~~~~~~~~~
llvmOpenmp/1.0  llvmOpenmp/2.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: containers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
singularity2/2.5.2
~~~~~~ category: cross webbrowser web application development environment ~~~~~~
chromedriver/2.46
~~~~~~~~~~~~~~ category: data analysis(http://www.mega-nerd.com/) ~~~~~~~~~~~~~~
libsnd/1.0.28
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: data science ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
catboost/0.8.1      manureadr/1.0       pandas/py_3.6.6
catboost/0.8.1_py3  pandas/py_3.5.2
~~~~~~~~~~~~~~~~~~~~~~~ category: data structure library ~~~~~~~~~~~~~~~~~~~~~~~
dcm2bids/2.1.4
~~~~~~~~~~~~~~~~~~~~~~ category: debugging and profiling ~~~~~~~~~~~~~~~~~~~~~~~
forge/18.2.3  forge/19.1.2  forge/21.0.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: dev ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
googletest/1.8.0  qemu/4.1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: devel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bazel/0.23.0           glew/1.13.0            opengl/nvidia-375.66
bazel/0.25.2           glew/2.1.0             opengl/nvidia-390.30
bazel/0.4.4            opengl/mesa-12.0.6     opengl/nvidia-410.72
bazel/0.5.4            opengl/mesa-18.3.3     protobuf/3.4.1
bazel/1.2.1            opengl/nvidia          protobuf/3.6.0_gcc5.4
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: editor ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
emacs/26.3    neovim/0.4.4
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: engineering ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
abaqus/2017                  comsol/5.6
abaqus/2021                  mentor-calibre/2020.1_36.18
abaqus/2021.1                spectre/191
abaqus/2021.1_intel17        su2/7.0.2
abaqus/6.12sp2               synopsys/2018.06
ansys/18.1                   synopsys/2020.06
assura/04.16.107             synopsys/L_2016.03-SP2
comsol/5.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: fMRI ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
brainiak/Feb2018
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: fonts ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
freetype/2.7.1
~~~~~~~~~~~~~~~~~~~~~~~ category: gene and species tree ~~~~~~~~~~~~~~~~~~~~~~~~
phyldog/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: genetics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
megacc/10.1.8
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: genomic ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
admixture/1.3.0           glactools/1.0.7           pybigwig/0.3.15
deeptools/3.2.1           partitionfinder/2.1.1     pysam/0.15.2
deeptoolsintervals/0.1.7  py2bit/0.3.0              pyslim/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: genomics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cellranger/3.1.0      cellranger/arc-1.0.1  faststructure/1.0
cellranger/5.0.1      chromopainter/0.0.4   relernn/6Dec2019
cellranger/6.0.0      clustal_omega/1.2.4
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: graphics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
campari/3.0        gnuplot/5.2.3      inkscape/Sep-2019
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: image ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
c3d/1.0.0          imagej/1.52a       leptonica/1.79.0   openexr/2.2.1
ffmpeg/3.2.4       imagemagick/7.0.7  libgd/2.2.5
ffmpeg/4.0.1       isis/3.5.1         libgit/1.1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~ category: image converter ~~~~~~~~~~~~~~~~~~~~~~~~~~~
mriconvert/2.1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~ category: image processing ~~~~~~~~~~~~~~~~~~~~~~~~~~
libgif/5.1.9            libjpeg-turbo/2.0.2     openjpeg/2.3.1
libjpeg/9.0             mayavi/4.6.0            openslide-python/1.1.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: languages ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
R/3.3.2               julia/1.0             miniconda/4.10
R/3.4.0               julia/1.0.2           perl/5.16.0
R/3.4.3               julia/1.1.0           perl/5.18.2
R/3.4.3_mkl           julia/1.2.0           perl/5.24.1
R/3.4.4               julia/1.4.1           perl/5.30.0
R/3.5.2               julia/1.4.2           perl/5.8.9
R/3.6.0               julia/1.5.0           python/2.7.12
R/3.6.3               julia/1.5.1           python/2.7.12_clean
R/4.0.0               julia/1.5.2           python/2.7.16
R/4.0.3               julia/1.5.3           python/3.5.2
R/4.0.5               julia/1.5.4           python/3.6.6
R/4.1.0               julia/1.6.0           python/3.6.6_test
anaconda/2-4.3.0      julia/1.6.1           python/3.6.8_gcc8.3
anaconda/2-5.3.0      julia/1.6.2           python/3.7.4
anaconda/2020.02      julia/1.6.3           python/3.8.12_gcc8.3
anaconda/3-4.3.0      lua/5.3.4             python/3.9.0
anaconda/3-5.2.0      maple/16              qt/3.3.8b
golang/1.15.6         maple/20              qt/3.3.8b-51
golang/1.16.6         materialstudio/2020   qt/5.10.1
golang/1.17.1         mathematica/10.3.1    qt/5.12
idl/8.5.1             mathematica/11.0      qt/5.12.0
idl_DEEPS/8.7.2       mathematica/12.0      qt/5.13.1
java/8u111            matlab/R2016a         qt/5.14.2
java/jdk-11.0.11      matlab/R2017a         qt/5.7.0
java/jdk-12.0.2       matlab/R2017b         qt/5.9.0
julia/0.5.1           matlab/R2018a         ruby/2.4.0
julia/0.6.1           matlab/R2018b         rust/1.45.1
julia/0.6.4           matlab/R2019a         rust/1.50.1
julia/0.7.0           matlab/R2021a         scala/2.12.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: lib ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
lftp/4.8.4
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Xvfb/1.20.12
acml/5.2.0-gfortran
agalma/1.0.0
armadillo/9.200.4
assimp/4.1.0
assimp/5.0.0
blast-legacy/2.2.26
boost/1.44.0
boost/1.49.0
boost/1.55
boost/1.57
boost/1.62.0-intel
boost/1.63.0
boost/1.68
boost/1.69
boost/1.75.0_openmpi_4.0.5_intel_2020.2_slurm20
boost/1.76.0_hpcx_2.7.0_gcc_10.2_slurm20
boost/1.76.0_hpcx_2.7.0_intel_2020.2_slurm20
bzip2/1.0.2
c-blosc/1.16.3
cdhit/4.6.8
cuda/10.0.130
cuda/10.1.105
cuda/10.2
cuda/11.1.1
cuda/11.1.1_intel_2020
cuda/11.3.1
cuda/7.5.18
cuda/8.0.61
cuda/9.0.176
cuda/9.1.85.1
cuda/9.2.148
ea-utils/1.04.807
fabm/1.0.2
fastq-tools/0.8
fftw/2.1.5
fftw/2.1.5-double
fftw/3.3.6
fftw/3.3.8
fftw/3.3.8a
freeglut/3.0.0
geos/3.4.2
geos/3.7.1
geos/3.8.1
gerris/1.0
global_arrays/5.6.1
global_arrays/5.6.1_i8
global_arrays/5.6.1_openmpi_2.0.3
global_arrays/5.8_openmpi_4.0.5_gcc_10.2_slurm20
gmp/6.1.2
gsl/1.15
gsl/2.3
gsl/2.5
igraph/0.7.1
keras/2.0.9
keras/2.1.1
keras/2.1.3_py3
lemon/1.3.1
leveldb/1.20
libcutensor/10.2
libflint/2.7.1
libint/2.5.0
libpng12/1.2.57
libspatialindex/1.9.3
libtiff/4.0.10
libxc/4.3.4
libzip/0.9-3.1.el6
lp_solve/5.5.2.5
metis/5.1.0
mpfr/3.1.5
mpfr/4.0.2
mpi4py/3.0.1_py3.6.8
mxnet/1.3.0
node.js/14.16.0
node.js/6.10.3
occa/1.2
openblas/0.2.19
openblas/0.2.8
openblas/0.3.7
opencv/3.2.0
opencv/3.4.1
parmetis/4.0.3
pcre2/10.35
phylobales/1.8b
proj/4.9.3
proj/5.2.0
proj/7.0.0
pytorch/1.3.1
sparsehash/2.0.2
suiteparse/4.5.4
sysstat/12.5.4
tensorflow/1.1.0_cpu
tensorflow/1.1.0_gpu
tensorflow/1.13.1_cpu_py3
tensorflow/1.13.1_gpu
tensorflow/1.13.1_gpu_keras
tensorflow/1.13.1_gpu_py3
tensorflow/1.14.0_gpu_py36
tensorflow/1.4.1_cpu
tensorflow/1.4.1_cpu_py3
tensorflow/1.4.1_gpu
tensorflow/1.4.1_gpu_py3
tensorflow/1.4.1_gpu_py3_cuda9.1
tensorflow/1.5.0_cpu_py3
tensorflow/1.5.0_gpu
tensorflow/1.5.0_gpu_py3
tensorflow/2.0.0_cpu_py37
tensorflow/2.0.0_gpu_py37
theano/1.0.1_py3
transdecoder/5.4.0
v8/3.14.5
voro++/0.4.6
vrpn/7.33
whatshap/Sep2018
xeyes/1.0
xz/5.2.4
yaml-cpp/0.6.2
yaml-cpp/0.6.2_intel2019.3
yaml-cpp/0.6.3_intel_2020.2
zlib/1.2.11
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: library ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
catch2/2.3           matplotlib/2.2.4     pmclib/1.1
dcmtk/3.6.6          multinest/3.10       pstokes/1.0
dotnet/5.0.202       nccl/2.4.7           sprng/5.0
easydict/1.7         nccl/2.8.4           statsmodels/0.9.0
fastq_screen/0.13.0  openslide/3.4.1
gsutil/350           p7zip/16.02
~~~~~~~~~~~~~~~~~~~~~~~~~~ category: machine learning ~~~~~~~~~~~~~~~~~~~~~~~~~~
1/1.01                            py-faster-rcnn/July2018
1/2.03                            rapidjson/1.1.0
attend2u/20180216                 scikit-learn/0.19.1
cudnn/5.1                         scikit-learn/0.21.2
cudnn/6.0                         sciml_class/pytorch-21.06
cudnn/7.0                         tensorflow/1.1.0_cpu
cudnn/7.4                         tensorflow/1.1.0_gpu
cudnn/7.6                         tensorflow/1.13.1_cpu_py3
cudnn/7.6.5                       tensorflow/1.13.1_gpu
cudnn/8.1.0                       tensorflow/1.13.1_gpu_keras
cudnn/8.2.0                       tensorflow/1.13.1_gpu_py3
deeparg/Jan2019                   tensorflow/1.14.0_gpu_py36
deeparg/Oct2018                   tensorflow/1.4.1_cpu
deeplabcut/1.01                   tensorflow/1.4.1_cpu_py3
deeplabcut/2.03                   tensorflow/1.4.1_gpu
deeplabcut/2.1.4                  tensorflow/1.4.1_gpu_py3
deeplabcut/2.1.9                  tensorflow/1.4.1_gpu_py3_cuda9.1
deeplabcut/2.2                    tensorflow/1.5.0_cpu_py3
dlib/19.17                        tensorflow/1.5.0_gpu
flann/1.8.4                       tensorflow/1.5.0_gpu_py3
hnn/1.0                           tensorflow/2.0.0_cpu_py37
horovod/0.16                      tensorflow/2.0.0_gpu_py37
horovod/0.19.5                    tf-horovod/1.0
pcl/1.9.1                         xgboost/1.3.3
pcl/1.9.1_nurbs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: math ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
atlas/3.10.3           lapack/3.7.0           ripser/0.5.3
blas/3.7.0             macaulay2/1.12-1       rss/1.0
cgal/3.14.1            magma/V2.23-10         sage/8.7
fenics/2017.1          magma/V2.25-5          sage/9.0
fenics/2018.1.0        magma/V2.25-5-gpu      scalapack/2.0.2
gap/4.9.1              magma-gpu/2.4.0        sympy/1.4
gmsh/3.0.1             magma-gpu/2.5.4_volta  trlan/2010.09
lapack/3.4.2           nlopt/2.5.0
lapack/3.6.0           numpy/intel_1.15.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: misc ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
mark/Dec18              mysql/8.0.13
mysql/5.7.28            netcdf4-python/4.1.4.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: ml ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bonito/0.3.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
gotm/5.0_qingli  gotm/5.3
~~~~~~~~~~~~~~~~~~~~~~~~~ category: molecular dynamics ~~~~~~~~~~~~~~~~~~~~~~~~~
mdanalysis/0.19.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: mpi ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
mpi/cave_mvapich2_2.3b_gcc
mpi/cave_mvapich2_2.3b_intel
mpi/cave_mvapich2_2.3rc2_gcc
mpi/hpcx_2.7.0_gcc_10.2_slurm20
mpi/hpcx_2.7.0_intel_2020.2_slurm20
mpi/mpich3.3a3_intel_2020.2
mpi/mvapich2-2.3.5_gcc_10.2_slurm20
mpi/mvapich2-2.3.5_intel_2017.0_slurm20
mpi/mvapich2-2.3.5_intel_2020.2_slurm20
mpi/openmpi_2.0.3_intel_2020.2_slurm20
mpi/openmpi_3.1.6_gcc
mpi/openmpi_3.1.6_gcc_10.2_slurm20
mpi/openmpi_4.0.0_gcc
mpi/openmpi_4.0.1_gcc
mpi/openmpi_4.0.4_gcc
mpi/openmpi_4.0.4_gcc_8.3_slurm20
mpi/openmpi_4.0.5_gcc_10.2_slurm20
mpi/openmpi_4.0.5_intel_2020.2_cuda_9.1.85_slurm20
mpi/openmpi_4.0.5_intel_2020.2_slurm20
mpi/openmpi_4.1.1_gcc_10.2_slurm20
osu-mpi/5.3.2
osu-mpi/5.6.2_mvapich2-2.3a_gcc
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: mri ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
afni/17.1.00                      freesurfer/6.0.0
afni/18.2.06                      freesurfer/6.0.0_rh7
afni/19.0.17                      freesurfer/6.0.0_rh7-30-Nov-2018
afni/19.3.08                      freesurfer/7.1.1
afni/19.3.10                      fsl/5.0.10
afni/20.0.03                      fsl/6.0.0
afni/20.1.06                      fsl/6.0.3
afni/21.2.04                      mmvt/2020-06
ants/2.1.0                        mricron/05-2016
ants/2.3.1                        mricron/12-2012
ants/2.3.4                        mricron/2019-09
dsi/april2019                     tortoise/3.1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: neuro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dtitk/2.3.1  qit/1.0      qit/2.0      qit/Jun21
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: neuroscience ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
sct/4.2.2
~~~~~~~~~~~~~~~~~~~~~~~ category: numerical optimization ~~~~~~~~~~~~~~~~~~~~~~~
bayop/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: other ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
sbt/1.2.3
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: package ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ffte/6.0      ffte/6.0/mpi
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: performance ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ior/2.10.3                       ipm/2.0.6_r
ior/3.0.1                        osu-mpi/5.3.2
ior/3.2.1                        osu-mpi/5.6.2_mvapich2-2.3a_gcc
ipm/2.0.6                        papi/5.4.3
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: physics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
casa/5.1.2
geant/4.10.04.p01
geant/4.10.3
geant/4.10.5
geant/4.9.4.p04
gpaw/0.10.0
gpaw/1.2.0
gpaw/1.2.0_hpcx_2.7.0_gcc
gpaw/1.2.0_mvapich2-2.3a_gcc
gpaw/20.10.0_hpcx_2.7.0_intel_2020.2_slurm20
gpaw/20.10_hpcx_2.7.0_intel_2020.2_slurm20
gpaw/21.1.0_hpcx_2.7.0_gcc_10.2_slurm20
gpaw/21.1.0_openmpi_4.0.5_gcc_10.2_slurm20
gpaw/21.1.0a_openmpi_4.0.5_gcc_10.2_slurm20
hoomd/2.9.0
jdftx/1.4.2
lumericalfdtd/8.16.982
mcx/20180525
mcxlab/2017.7
mujoco-py/1.50.1.23
pymultinest/2.9
root/6.10
~~~~~~~~~~~~~~~~~~~~~~ category: profiling and debugging ~~~~~~~~~~~~~~~~~~~~~~~
cube/4.3.4                 ncdu/1.14
ddd/3.3.12                 scalasca/2.3.1_intel
gdb/7.12.1                 scorep/3.0_intel_mvapich2
gperftools/2.5             valgrind/3.12.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: python ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
numpydoc/0.9.1       pytables/3.5.2       six/1.12.0
plotly/3.9.0         python_igraph/0.7.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: python library ~~~~~~~~~~~~~~~~~~~~~~~~~~~
wx/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: software ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
annovar/2018Apr16  orthofinder/2.2.7  orthofinder/2.3.3
~~~~~~~~~~~~~~~~~~~~~~~~ category: software management ~~~~~~~~~~~~~~~~~~~~~~~~~
maven/3.2.2  maven/3.8.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: solvers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
eigen/3.2.2
eigen/3.3.2
eigen/3.4.0
mcl/12.135
mineos/1.0
mumps/5.0.2
mumps/5.0.2-seq
openfoam/4.1
openfoam/4.1a
openfoam/7.0_hpcx_2.7.0_gcc_10.2_slurm20
pari/2.11.2
pastix/5.2.3
petsc/3.14.2_hpcx_2.7.0_gcc_10.2_slurm20
petsc/3.14.2_hpcx_2.7.0_intel_2020.2_slurm20
petsc/3.14.2_mpich3.3a3_intel_2020.2
petsc/3.7.5
petsc/3.7.7
petsc/3.8.3
polyrate/17C
scotch/6.0.4
suiteparse/4.5.4
trilinos/12.12.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: stat ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
seaborn/0.10.0
~~~~~~~~~~~~~~~~~~~~ category: statistical analysis package ~~~~~~~~~~~~~~~~~~~~
glmnet/1.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: statistics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
JAGS/4.2.0            R/3.6.3               caffe/1.0_with_cudnn
JAGS/4.3.0            R/4.0.0               mallet/2.0.8rc3
R/3.3.2               R/4.0.3               rstudio/1.0.44
R/3.4.0               R/4.0.5               rstudio/1.1.463
R/3.4.3               R/4.1.0               rstudio/1.4.1103
R/3.4.3_mkl           SAS/9.4M6             stata/14
R/3.4.4               SAS/9.4b              stata/15
R/3.5.2               caffe/1.0             wfu_pickatlas/3.0.5b
R/3.6.0               caffe/1.0_CPU_ONLY
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: sys ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dos2unix/7.4.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: system ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
wayland/1.18.0          xfce/4.10               xfce/4.16
wayland-protocols/1.20  xfce/4.12
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: tex ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandoc/2.9.2.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: text editing ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
texstudio/2.12.16
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: tool ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dcm2niix/25.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: tool kit ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
subread/1.6.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: tools ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
idr/2.0.2    omero/5.6.2
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: utilities ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
atom/1.19.3                   git/2.10.2
bamaddrg/20180928             git/2.20.2
binutils/2.29.1               git/2.29.2
binutils/2.31                 h4cf/1.2
chrome/55.0                   h5py/2.9.0
chrome/73.0                   intltool/0.51.0
cmake/3.10.1                  itstool/2.0.4
cmake/3.15.4                  json_cpp/1.9.4
cmake/3.20.0                  json_fortran/8.1.0
cmake/3.6.3                   kallisto/0.46.1
cmake/3.8.0                   libevent/2.1.8
colordiff/1.0.18              libwnck/3.24.1
comsol/5.2                    mercurial/5.1
comsol/5.6                    ncurses/6.2
cppunit/1.14.0                pdftk/2.02
curl/7.61.1                   perf-tools/2.7
cvs/1.11.23                   rclone/1.51.0
depot_tools/Jan2019           rsync/3.1.3
engineering/19.10.237         spfft/0.9.12
engineering/calibre2020.1_36  sublime/2.0.2
express/1.5.1                 svn/1.8.17
fileZilla/3.10.0              svn/1.9.5
firefox/56.0.2                texinfo/4.13a
firefox/59.0.2                texlive/2018
firefox/66.0.3                vim/8.1
firefox/68.0                  vim/8.1_py3
firefox/87.0                  vnc-apps/7.2
ghostscript/9.21              xxdiff/4.0.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: utility ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
redis/6.2.4
~~~~~~~~~~~~~~~~~~~ category: version record for large files ~~~~~~~~~~~~~~~~~~~
gitlfs/2.7.1
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: vision ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
opencv/3.2.0         scikit-image/0.13.1  tesseract/3.05.00
opencv/3.4.1         scikit-image/0.15.0  tesseract/4.00.00
~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
atomeye/3.0
basemap/1.2.0
blender/2.78
blender/2.79
blender/2.90.1
cartopy/0.16.0
cartopy/0.17.0
cave-demo/yurt
cave-utils/yurt
caviar/1.0
ferret/7.1
gdal/2.1.3
gdal/2.4.0
gdal/3.0.4
gephi/0.9.2
gimp/2.8.22
gimp/2.9.6
glm/0.9.9
glm/0.9.9.5
grace/5.1.25
graphviz/2.40.1
minvr/0.3master
minvr/0.4
minvr/beta
minvr/beta-bleeding
minvr/dev_ben
ncview/2.1.7
openscad/2015.03-3
openscenegraph/3.4.0
osgearth/2.7
ovito/2.8.2
ovito/2.9.0
paraview/5.1.0
paraview/5.1.0_yurt
paraview/5.4.1
paraview/5.6.0_no_scalable
paraview/5.6.0_yurt
paraview/5.8.0
paraview/5.8.0_mesa
paraview/5.8.0_release
paraview/5.8.1_openmpi_4.0.5_intel_2020.2_slurm20
paraview/5.9.0
photoscan/1.3.0
pyshp/2.0.1
shapely/1.6.4
tecplot/2017
tecplot/2017R3
tecplot/2018R2
tecplot/2019R1
unity/Unity-2017.3.0b1
unity/unity-editor-2017.2.0f3
visit/2.13.2
visit/2.7.2
vmd/1.9.3
vrg3d/265
vrg3d/265_compat
vrg3d/265_demos
vtk/5.6.1
vtk/7.1.1
vtk/7.1.1a
vtk/8.1.0
xcrysden/1.5.60
~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: vizualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
metashape/1.5.4
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: weather ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
wrf/3.6.1
wrf/4.2.1_hpcx_2.7.0_intel_2020.2_slurm20
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ category: workshop ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
workshop/1.0  workshop/3.0

Oscar Software Modules

Oscar: Sage

Loading and Launching Sage

1. Once authenticated to Oscar, use the following commands at the command line.

2. Start an interactive job by using the interact command. This command can take additional parameters to extend the resources and time allotted to the node as well as the partition that the node operates on.

3. The Sage module provides containers. To load them use module load sage-container/10.3.

4. To start the container use apptainer shell /oscar/rt/9.2/software/0.20-generic/0.20.1/opt/spack/linux-rhel9-x86_64_v3/gcc-11.3.1/sage-container-10.3-avpqipfsnbneig726l72jrgdmlrivg4m/sage.sif

5. Once inside the container's shell use sage to launch the Sage console.

Sage on Oscar OnDemand

The easiest way to run Sage on Oscar OcDemand is to run sage in an interactive job via the terminal in your OnDemand session.

Use the interact command with parameters for your specific job to start the interactive session, then load your modules and run the sage binary (steps 2-4 above).

interact -n 2 -m 32g -t 04:00:00 -f 'haswell|broadwell|skylake'

Using Sage with Batch Scripts

Thanks to Trevor Hyde from Summer@ICERM 2019 for these instructions.

One method for running computations with Sage on Oscar is to write a script and use the slurm batch scheduler to have Oscar run your script. This requires two pieces:

A shell script to configure and submit your batch job to the cluster.
Your Sage code/program you'd like to run.

Example Batch Script

sage-batch.sh

#!/bin/bash
 
#SBATCH -J test_program
#SBATCH --array=0-9
#SBATCH -t 1:00:00
#SBATCH --mem=8G
 
#SBATCH -e data/<oscar-username>/test_output/test%a.err
#SBATCH -o data/<oscar-username>/test_output/test%a.out
 
module load sage-container/10.3

apptainer shell /oscar/rt/9.2/software/0.20-generic/0.20.1/opt/spack/linux-rhel9-x86_64_v3/gcc-11.3.1/sage-container-10.3-avpqipfsnbneig726l72jrgdmlrivg4m/sage.sif
 
sage test_program.sage $SLURM_ARRAY_TASK_ID

#!/bin/bash tells the system this is a bash (shell) script.
#SBATCH -J test_program sets the name of the job which appears when you check the status of your jobs.
#SBATCH –array=0-9 is an easy way of doing parallel computations. In this case it says our job will run on 10 different nodes, each node will be passed a parameter and we have specified that the parameters will take the values 0 through 9. You can specify several ranges or even list individual parameters if you prefer.
#SBATCH -t 1:00:00 specifies a time limit in HH:MM:SS for each node. Once this time runs out your program will stop running on that node. Be careful setting the time limit too high as doing so may make it take a long time for your job to get scheduled to run. Before starting a big computation try to do some smaller tests to see how long you expect to need.
#SBATCH –mem=8G specifies how much memory each node gets. Standard exploratory accounts get 123GB total to use at any one time. So if you allocate too much per job, fewer jobs will run at once. On the other hand, if you allocate too little and a computation needs more than it has, then it will terminate. If this happens an “out of memory” error will show up in the .err file for that node.
#SBATCH -e data/<ccv-username>/test_output/test%a.err and #SBATCH -o data/<ccv-username>/test_output/test%a.out specify where the error messages and output for each computation should be sent. You should store these files in your user folder, not on the submit node. We each have a folder inside the data directory which you can see from the submit node. In this example I have created a folder titled test_output where I’m putting both of these files. You need to make these folders before you run the computation otherwise the output will be dumped into the void! The %a will get replaced with the array parameter. So for example, since we set our array parameters to be 0-9 there will be 10 nodes running and each of them gets a number between 0 and 9; this node corresponding to the parameter 7 will create two files test7.err and test7.out.
module load sage-container/10.3 loads the sage container into the node.
apptainer shell /oscar/rt/9.2/software/0.20-generic/0.20.1/opt/spack/linux-rhel9-x86_64_v3/gcc-11.3.1/sage-container-10.3-avpqipfsnbneig726l72jrgdmlrivg4m/sage.sif initiates the container's Sage console shell.

Everything after this in the script happens as if you typed it yourself onto the command line.

In our example, we want to run sage code, so the line sage test_program.sage $SLURM_ARRAY_TASK_ID runs our example sage program test_program.sage.
The file needs to have the .sage extension.
You should write this file in a text editor, not in a Jupyter notebook (although you can first write and test your program in a Jupyter notebook and then copy and paste it into a new file when it’s ready).
This program is written to accept one input and I have passed it $SLURM_ARRAY_TASK_ID which is the array parameter passed to each node. You can use this parameter to select which input parameters to run your program on.

Example Sage Program

test_program.sage

import sys
 
def fun_math(message):
    print message
    sys.stdout.flush()
 
job_id = int(sys.argv[1])
fun_math('hi this is a test')
fun_math('my job id is' + str(job_id))

In the Sage program, you first define all of your functions and then you include the code you want to run.
Import sys so you can access the array parameter passed to your function from the node. This is accessed in this case by sys.argv[1]. Make sure you explicitly coerce to be an integer if you want to use it as an integer; it's a string by default.
The output of the print command is appended to the .out file for this node as a new line.
Notice the line sys.stdout.flush() included in the function. This makes the program immediately send whatever output it has to the output file when called. Otherwise the program won’t output anything until it has completely finished running. If each node is running 100 potentially long computations and it finishes the first 99 but then times out on the 100th computation, and you don’t include any sys.stdout.flush() commands, everything will be lost when time runs out.

Submitting the Batch Job

To run this batch program go back to the submit node and type sbatch <NAME_OF_BATCH_FILE>. In our example here, our batch file is called sage-batch.sh, so we simply type sbatch sage-batch.sh Slurm will return a line that tells you your job has been submitted together with a job id number.
To check the progress of your jobs type myq from anywhere on Oscar. This will show you what jobs you have running, how much time they have left, and which jobs are still waiting to run. Be patient, sometimes it takes a minute for things to get started.
If you realize your code is never going to finish or that you’ve made some terrible mistake, you can cancel a batch job by typing scancel <JOB_ID>. You can specify a single node or just put the general job id for the whole run and cancel everything.

Oscar Software Modules

Oscar: MATLAB

Loading and Launching MATLAB

1. Open the Terminal and use the following commands at the command line.

2. module avail matlab to list all the available matlab versions.

3. module load matlab/R2018a to load matlab. This command should return the confirmation “module loaded”.

4. matlab to launch the MATLAB app.

Installing MATLAB Packages such as YALMIP

MATLAB script packages, such as YALMIP, can be installed directly by the user on their Oscar account. These instructions assume you are using Oscar via the VNC client.

1. Open the Terminal on your VNC session.

2. Navigate to your home folder by typing cd ~

3. mkdir -p MATLAB

4. wget -O yalmip.zip https://github.com/yalmip/yalmip/archive/master.zip

5. unzip yalmip.zip

6. In MATLAB, add the YALMIP-master directory to your path.

In the MATLAB file browser, navigate to the MATLAB folder you created in your home folder. cd ~/MATLAB
Right click on the YALMIP-master folder.
Select Add to Path > Selected Folders and Subfolders. This adds the YALMIP folders to your path.

7. To save your MATLAB path, use the savepath command in the MATLAB command prompt. savepath ~/MATLAB/pathdef.m

YALMIP also requires a solver like SDPT3. The steps below add SDPT3 to MATLAB.

1. Open the Terminal.

2. cd ~/MATLAB

3. wget -O sdpt3.zip https://github.com/sqlp/sdpt3/archive/master.zip

4. unzip sdpt3.zip

5. In MATLAB, add the sdpt3 directory to your path.

In the MATLAB file browser, navigate to the MATLAB folder you created in your home folder. cd ~/MATLAB
Right click on the sdpt3-master folder.
Select Add to Path > Selected Folders and Subfolders. This adds the SDPT3 folders to your path.

6. To update/save your MATLAB path, use the savepath command in the MATLAB command prompt. savepath ~/MATLAB/pathdef.m

Oscar Software Modules

Oscar: Mathematica

Loading and Launching Mathematica

1. Open the Terminal and use the following commands at the command line.

2. module avail mathematica to list all the available mathematica versions.

3. module load mathematica/11.0 to load mathematica. This command should return the confirmation “module loaded”.

4. mathematica to launch the Mathematica app.