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

Currently Available Modules
Oscar: Sage
Oscar: MATLAB
Oscar: Mathematica
Oscar: R
Oscar: Macaulay 2
Oscar: Magma

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 Oscar OnDemand through a web browser.

Using Oscar

Oscar provides both command line (SSH) access and VNC through Open OnDemand 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 CCV resources 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 is a web portal to the Oscar computing cluster. This is the easiest way to access a number of CCV resources including an Oscar Shell, interactive applications like MATLAB, and a fully featured Linux GUI. See the OOD Login Instructions for information on how to get started with the web portal.

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
Macaulay 2
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. When using Oscar over the Linux GUI, you must open the Terminal Emulator to run these commands.

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.

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 command: 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>
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.

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.
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.

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.

SSH Access to Oscar

1. Open your preferred terminal application. Windows users need to install an SSH client. We recommend PuTTY, a free SSH client for Windows.

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.

Additional Information:

1. To authenticate to Oscar with SSH keys, refer to this documentation.

2. 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.

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.
Workflow remains the same with or without VPN.

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

Oscar Software Modules

Below are instructions for software modules available on the Oscar clutter.

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 May 6, 2025. Please scroll across the code snippet to see the full list. For the most up-to-date list of software modules, log into your Oscar account and run the command module avail.

Note: D is the default module.

abaqus-container/2021-akaeexs                    filezilla/3.49.1-epfjuus                       mafft/7.505-iuicuiv                         qit/2023-04-04-grwuvgg
   abaqus/2017-q4ghhm5                              flashpca/2.0-zr2wflq                           magma-usyd/V2.28-8-p3zylpg                  qmcpack-mpi/3.16.0s-qp2hymx
   abaqus/2021.1-i675dvw                            freebayes/1.3.6-v7rppcd                        magma/2.7.1-cpueyjy                         qscintilla/2.11.6-dq7zlcq
   abaqus/2024-h5273a3                       (D)    freeglut/3.2.2-76qqoqn                         maple/22-cp2uld4                            qt/5.15.9-fb7mjex
   abaqus/2024-ir-a4m5ld5                           freesurfer/7.3.2-zop5n6m                       mark/2018.07.08-43snzfu                     qualimap/2.2.1-mybpdoi
   abaqus/2024-mbessa-ceayfuo                       freesurfer/8.0.0-jotmypd                (D)    materialstudio/2024s-gbc3dg6                quantum-espresso-mpi/7.1-gits-v4bxgtv
   abaqus/2024.1-7pcdqhp                            fsl/6.0.7.7s-bul4mby                           mathematica/13.2.0-n4i6yua                  quantum-espresso-mpi/7.1s-ia43pjk
   admixture/1.3.0-onwaqrp                          fv/5.5.2-g2ibb5x                               matlab/R2019a-rjyk3ws                       quantum-espresso-mpi/7.3s-kydgjwo     (D)
   afni/23.3.07s-zm43m3u                            fzf/0.45.0-pdwl7a4                             matlab/R2023a-xd6f7ph                (D)    r/4.0.0-p7gxu4e
   afni/24.2.01s-kstpoqt                     (D)    gatk/4.3.0.0-234wqft                           matlab/R2024b-ipaztju                       r/4.0.3-pvf2znb
   anaconda/2023.09-0-7nso27y                       gaussian/09_v1-u6klkps                         maven/3.8.4-w3zgh4v                         r/4.1.0-bfjsvw5
   angsd/0.935-cbhuwc7                              gaussian/09-D01_v2-tw73726                     mercurial/5.8-vly6btb                       r/4.2.2-z6qdiis
   ant/1.10.13-alpqj4j                              gaussian/09-D01-TEST_v3-vv6ar67                mesa/22.1.6-yi2tztm                         r/4.3.1-lmofgb4
   ants/2.4.3-75npyop                               gaussian/16-C01-bb2r2gh                 (D)    meson/1.6.0-eipcwzq                         r/4.4.0-yycctsj
   aria2/1.36.0-lsb7zcs                             gaussview/v05-mkdyw6j                          metis/5.1.0-7qoahod                         r/4.4.2-re5rjx3                       (D)
   arm-forge/22.1.3-zq7lvdq                         gcc/6.5.0-lwshmxc                              miniconda3/23.11.0s-odstpk5                 raisd/2.9-svyic22
   armadillo/12.2.0-4clpczv                         gcc/10.1.0-mojgbnp                             miniforge/23.11.0-0s-hwmjdtj                rclone/1.62.2-o4lkrv6
   atom/1.19.3-ty5sdsn                              gcc/13.1.0-nvrtbp3                      (D)    minimap2/2.14-33hmvx2                       readline/6.3-rnqups2
   autoconf/2.69-p4rpdx2                            gcm/2.4.1-lfqoarh                              molden/6.7-isryqwj                          readline/8.2-5xbuyjt                  (D)
   autoconf/2.71-opdgqnq                     (D)    gdal/3.7.0-4p4onmf                             molden/7.3-kytvh3m                   (D)    root/6.28.04-u7t5ax7
   avogadro2/1.99.0-5zl5qaw                         gdal/3.10.3-4vc4f76                     (D)    molpro-mpi/2023.2.0s-vyfv74n                rsem/1.3.3-5obucw6
   awscli/1.27.84-v22kngs                           geeqie/2.4-6vdnc4v                             molpro-mpi/2024.3.1-mpipr-hwakoux    (D)    rstudio/2023.09.1-lsqy746
   bamtools/2.5.2-ki3mdef                           geos/3.11.2-a6hfu6a                            mpc/1.3.1-zbino7j                           ruby/3.1.0-gnoxsfm
   basilisk/2023.11.11s-x4isdvp                     ghostscript/10.0.0-3atesdh                     mpfr/4.2.0-n2tkxso                          rust/1.73.0-647r2tw
   bazel/6.1.1-vvtxktr                              gimp/2.10.32-tlknk2n                           mriconvert/2.1.0-oaq24fz                    rust/1.81.0-3qqoayc                   (D)
   bbmap/39.01-jnnkpwk                              git-lfs/3.3.0-laphnvj                          mricrogl/2022.07.20-n3b7whc                 sage-container/10.3-avpqipf
   bcftools/1.13-76jesdj                            git/2.44.0-6f7n7ni                             mricron/201909-3s2phrj                      sage/9.5-drpqjkh
   bcftools/1.16-ewu6fpe                     (D)    glew/2.2.0-plawm2j                             msmc2/2.1.4-cuac55f                         sage/10.3-nntihfr                     (D)
   bcl2fastq2/2.20.0.422-z3wh636                    glm/0.9.9.8-m3s6sze                            multiwfn/3.8_1208-qn65pif                   salmon/1.9.0-itdua6n
   beagle/5.4-e43mqsa                               glpk/5.0-zifs7bb                               mummer4/4.0.0rc1-4llgadq                    salmon/1.10.1-43ljn7g                 (D)
   bedops/2.4.40-bjb2v2n                            gmap-gsnap/2024-08-20-dur7jyc                  muscle/3.8.1551-pys2b76                     samtools/1.12-4v4uiz6
   bedtools2/2.31.0-lsohc7s                         gmp/6.2.1-qlaig4m                              mysql/8.0.29-w7xdbde                        samtools/1.16.1-txuglks               (D)
   bismark/0.23.0-eoksupu                           gnuplot/5.4.3-pdiiquy                          nanoflann/1.4.3-u2l24dv                     sas/9.4m8-f2f3xdp
   blast-legacy/2.2.26-tcdku3a                      go/1.17.1-f4mqosa                              nbo/7.0-phausw3                             schmutzi-container/1.5.7-vsorpcq
   blast-plus/2.2.30-cyxldrt                        go/1.20.3-xknmcqd                              nccl/2.16.2-1-gjmprw5                       schmutzi-container/1.5.7-3imwf7h      (D)
   blat/37-ebfj5e6                                  go/1.23.3-d3wvs6z                       (D)    ncdu/1.18.1-uofylp6                         schrodinger/2023-4-h3kvbn3
   blender/4.4.0-446jdgt                            google-cloud-cli/456.0.0-3mtj4z6               nco/5.1.5-36hru5t                           schrodinger/2024-1-yqi27-m4qqm46      (D)
   boost/1.80.0-harukoy                             gperf/3.1-56q4xf5                              ncview/2.1.8-nxepxtw                        scons/4.5.2-housgyw
   bowtie/1.3.1-2kd7din                             grace/5.1.25-duvo7rn                           neovim/0.9.4-67stov2                        seqkit/0.10.1-qtiftw4
   bowtie2/2.4.2-xdquyzq                            graphviz/8.0.1-75znavc                         neovim/0.10.4-7b4mer5                (D)    seqtk/1.3-kbdjwob
   bowtie2/2.5.3-qgscc2u                     (D)    gsl/2.7.1-khmyfcy                              netcdf-c/4.9.2-cfggqwi                      shapeit4/4.2.2-3us45un
   brotli/1.0.9-h22dril                             guppy/6.0.1-wpaqayj                            netcdf-c/4.9.2-4ozokng               (D)    singular/4.4.0-aeloppr
   bwa/0.7.17-lu4b4dj                               guppy/6.1.2-wwvvdfu                     (D)    netcdf-cxx4/4.3.1-6gcdg5s                   skewer/0.2.2-nwhklgr
   bxh-xcede-tools-container/1.11.14-4sphv7n        gurobi/10.0.1-q7rc5dw                          netcdf-fortran/4.6.0-kl27oji                slicer/5.4.0-rb2kk4l
   cadence/IC06.18-calibre2022.2-ascb7dw            hdf5/1.12.2-s6aacp3                            netcdf/4.9.2-ar77jpt                        slim/4.0.1-kymgtmu
   cadence/IC06.18.090-6famfci                      hdf5/1.14.1-2-rdd6y6v                   (D)    netlib-lapack/3.11.0-jdzmstx                slim/4.3-u22vcwu                      (D)
   cadence/IC23.10.000-ppqll2n               (D)    hisat2/2.2.1-gn4pb3l                           netlogo/6.4.0-psm765m                       spdlog/1.11.0-qbi24my
   casa/6.6.0-20-py3.8.el7-dqvn5lw                  homer/4.11.1-fpjs4l4                           netpbm/10.73.43-m2jdopk                     splash/2.1.4-unrsfpj
   cdhit/4.8.1-bqmf4jf                              hpcx-mpi/4.1.5rc2-mts-ukpby4i                  ngc-jax/23.10-paxml-py3-ziphcif             spm/8-77b5myx
   cellranger/arc-2.0.1-uamrhhu                     hpcx-mpi/4.1.5rc2s-yflad4v              (D)    ngc-pytorch/24.03-py3-a6ptiby               spm/12_r7606-prcq7fg                  (D)
   cellranger/atac-2.0.0-m2tfcpk                    htop/3.2.2-kqsjlaj                             ngc-tensorflow/24.03-tf2-py3-lmnuwwg        sratoolkit/3.0.0-u4jvgps
   cellranger/6.0.0-dbztt7r                  (D)    htslib/1.12-ecidzx4                            ninja/1.11.1-k2aq3rl                        stacks/2.65-geg4r7a
   cfitsio/4.2.0-5grfqtu                            htslib/1.17-zxcat2k                     (D)    nlopt/2.7.1-fwj27pk                         star/2.7.10b-fj6kao2
   cgal/5.4.1-64mikhl                               idba/1.1.3-nrxiqtw                             nnn/4.9-r7kawsv                             stata/mp17-v7a7uoo
   chrome/119.0.6045.159s-avadhvk                   idemp/201706-a45gc3d                           node-js/18.12.1-qkps4za                     stata/mp18-3wq5b4o                    (D)
   cli11/2.3.2-pcucv7l                              idl/8.9s-jocgnbh                               nvhpc/23.3-xa4nyqi                          stow/2.4.0-y2q7tsn
   clustal-omega/1.2.4-mbj3dq5                      igraph/0.7.1-wbiepb3                           nvhpc/23.7-alnzdzw                          stringtie/2.2.1-7uti3ny
   cmake/3.6.1-il7bkvj                              imagej/154-linux64-java8-jd6sflr               nvhpc/25.1-scvc2ac                   (D)    sublime-text/4.4143-im3loi3
   cmake/3.26.3-xi6h36u                      (D)    imagemagick/7.1.1-3-ex4k4u2                    nvtop/3.0.1-7r22pjl                         subread/2.0.2-5agghnd
   cnvnator/0.4.1-w3bkqjf                           inkscape/1.3s-gshcpwc                          octopus-lunter/0.7.4-kkqrfv3                swig/4.1.1-bq46cxl
   code-server/4.20.0-tcrmrcm                       intel-oneapi-compilers/2023.1.0-a7fw7qt        ollama/0.3.14s-a3gonhs                      synopsys/2023.12-df4a3ab
   code-server/4.97.2-33bvsj5                (D)    intel-oneapi-mkl/2023.1.0-xbcd2g3              ollama/0.5.12s-mjarasi               (D)    synopsys/2024.09-q3jwzot              (D)
   colordiff/1.0.21-ifskyqr                         intltool/0.51.0-vanhjsr                        openbabel/3.1.1-nay2mkb                     tabix/2013-12-16-d6qvxp7
   comsol/5.2-ufifhtv                               iq-tree/2.1.3-gu64b4j                          openblas/0.3.23-u6k5fey                     tcl/8.6.12-dziqp2l
   comsol/5.6_yqi27-jnspqto                         iq-tree/2.3.6-fn5vscb                   (D)    opencv/4.6.0s-5z4piup                       tcsh/6.24.10-dtqo5ky
   comsol/6.3_yqi27-7yf67lt                  (D)    iraf/2.17.1s-2r7ypc5                           openexr/3.1.5-6fapou6                       tecplot/2022r1-q5cg2zq
   conn/22a-nztrdv3                                 itk-snap-container/4.0.2-bychj73               openjdk/11.0.17_8-nw5ylvi                   tesseract/4.1.1-l2ejycz
   connectome-workbench/1.5.0-t66riqu               jags/4.3.1-4mvaxc3                             openjdk/17.0.5_8-pq2e7ao             (D)    tesseract/5.3.3-vq3altt               (D)
   cppunit/1.14.0-h3hsjgu                           jellyfish/2.2.7-dywzm7z                        openjpeg/2.5.0-iyu5vwb                      texlive/20220321-pocclov
   crossrate-container/2016-27ofi4r                 jo/1.9-ki7xc2u                                 openmpi/4.1.2-s5wtoqb                       texstudio/3.0.1-64vxo64
   cuda/10.1.243-bxisbai                            json-fortran/8.3.0-ehkzpjv                     openmpi/4.1.4s-smqniuf                      tk/8.6.11-uqpqlly
   cuda/10.2.89-xnfjmrt                             jsoncpp/1.9.5-vhsa2iy                          openmpi/4.1.5-kzuexje                       tmux/3.3a-zyhjvvh
   cuda/11.8.0-lpttyok                              julia/1.9.3s-i3zndt3                           openmpi/5.0.1s-6ti4ij7                      tmux/3.5a-kcjtgnd                     (D)
   cuda/12.1.1-ebglvvq                              julia/1.11.1s-kueea5s                   (D)    openmpi/5.0.2s-mfj2kfp               (D)    tn93/1.0.12-tcvbyl4
   cuda/12.2.0-4lgnkrh                              kraken/1.1.1-e6r2aej                           openslide/3.4.1-pzjb2kl                     tree/2.1.0-7tlhzo7
   cuda/12.3.0-r72aozf                              lemon/1.3.1-jh3h4xt                            openssl/1.1.1t-u2rkdft                      trimal/1.4.1-ace7du2
   cuda/12.4.0-piq32fy                       (D)    leptonica/1.81.0-pebiyok                       or-tools/9.10-k4nov4d                       trimgalore/0.6.6-iwfrq4c
   cudnn/7.5.1.10-10.1-hv4e2lt                      leveldb/1.23-f76iwfr                           ovito/3.6.0-rile7ax                         trimgalore/0.6.9-hisz5xp              (D)
   cudnn/8.7.0.84-11.8-lg2dpd5                      lftp/4.9.2-vimt4vf                             p7zip/17.05-3xtimiz                         trimmomatic/0.39-w5jnhai
   cudnn/8.9.6.50-12-56zgdoa                        libarchive/3.6.2-mnc5shn                       pandoc/2.19.2-wawlx5m                       udunits/2.2.28-rycabdx
   cudnn/9.8.0.87-12-j5i4iki                 (D)    libbeef/Nov2020-xhrdwg5                        pangolin/0.6-vwij3iv                        usearch/11.0.667-wx6utmj
   cufflinks/2.2.1-ogzw3z5                          libdeflate/1.10-5yi7m3g                        parallel/20220522-5ah2i5h                   v8/3.14.5-aompxje
   cutensor/1.5.0.3-gqkzath                         libgd/2.2.4-2iyhgxa                            paraview/5.9.0s-dgv24kr                     vasp-mpi/5.4.4-mdh3hpy
   datamash/1.8-ib4aakp                             libgd/2.3.3-ubu4k2f                     (D)    patchelf/0.17.2-aqmx4qb                     vasp-mpi/5.4.4-wannier-cqhzfma
   dcm2niix/1.0.20220720-nwsidfo                    libgeotiff/1.6.0-voueb6b                       paup/4.0a168-cwt24ux                        vasp-mpi/6.3.2_avandewa-chn3w3j
   diamond/2.0.15-h7xx24l                           libgit2/1.6.4-a432pgi                          pcre2/10.42-xks64jg                         vasp-mpi/6.4.2_cfgoldsm_vtst-cff5qmk
   dicombrowser/20181217s-ikvqhyr                   libiconv/1.17-jwjcds2                          pdftk/2.02-gu7lpeg                          vasp-mpi/6.4.2_cfgoldsm-7krhcss
   dlib/19.22-lxah7rq                               libjpeg-turbo/2.1.5-sewtk5u                    pdsh-chaos/23.12-t6ywlrp                    vasp-mpi/6.4.3_cfgoldsm-5dioee2
   dmtcp/3.0.0-xvfukfp                              libjpeg/9e-6djp5nd                             perl-dbi/1.643-t74vmeb                      vasp-mpi/6.4.3_yqi27-wannier-livyes6
   dorado/0.8.2-s42dhri                             libnsl/1.3.0-calriiy                           perl/5.26.2-o4iq4b4                         vasp-mpi/6.4.3_yqi27-6uzdgwn          (D)
   dos2unix/7.4.2-5a6dlgt                           libnsl/2.0.1-ed2i5hn                    (D)    perl/5.36.0-bt34quz                  (D)    vcftools/0.1.14-syssqsi
   dotnet/8.0.100-5lr7bga                           libpng/1.2.57s-lve65hz                         perl/5.37.9-og4osvm                         vim/9.1.0867-wl3haj7
   dropest/0.8.6-ewwx5ik                            libpng/1.5.30-ru3zswz                          perl/5.40.0-o7hxci2                         virtualgl/3.1-yphbrfj
   ds/9.8.5s-zpqg2jy                                libpng/1.6.39-ryxiwrd                   (D)    picard/2.26.2-qabtyqy                       visit-container/3.3.3-qz3dni6
   dsi-studio/chen-2023-sif-lytwlk2                 libreoffice/7.2.2.sif-drpjygp                  pigz/2.7-zgdlry3                            visit-mpi/3.3.3s-lz2dp7m
   dtitk/2.3.1s-bp7yqjh                             libsdl/2.30.7-4rxs72s                          plink/1.9-beta6.27-nvy4vrx                  vmd/1.9.3-oin2dnj
   eigen/3.4.0-uycckhi                              libsodium/1.0.20-4o75gk3                       plink/2.00-b6x44xw                   (D)    vscode/1.84.2-4tfimgp
   eigensoft/7.2.1-6ctbhoz                          libtiff/4.5.0-g6fga7e                          popoolation2/1.205s-luyjn2b                 vscode/1.97.2-txhgk3l                 (D)
   emacs/28.2-rwds2pd                               libtree/3.1.1-yxst452                          postgresql/16.4-vzmexkn                     wcstools/3.9.7-lo4forb
   expat/2.5.0-zujcztp                              libvips/8.13.3-ex4pfpg                         prodigal/2.6.3-vbq7usx                      wxwidgets/3.2.2.1-mk5eiyq
   fastme/2.1.5.1-kmg5til                           libwnck/3.24.1-4gvyjhg                         proj/9.2.0-ni5rcfb                          xcrysden/1.5.60-nuxe46i
   fastp/0.23.4-xmfbk37                             libx11/1.8.4-qdzkebe                           protobuf/3.22.2-6hlkkut                     xerces-c/3.3.0-djupwmt
   fastq-screen/0.15.3-7ymgrux                      libxc/4.3.4-uy5ogwb                            py-ase/3.21.0-pyuljod                       xeyes/1.2.0-nge56yb
   fastqc/0.11.9-mvd2uhw                            libxc/5.2.3-ncc5ir4                     (D)    py-matplotlib/3.7.1-4afsjsz                 xgboost/1.6.2-fp3ii65
   fastqc/0.12.1-sk2rb3a                     (D)    libyaml/0.2.5-wdpye7g                          py-statsmodels/0.13.2-sbdhj4k               yaml-cpp/0.7.0-6nno2ru
   fasttree/2.1.11-o5kvig7                          libzip/1.3.2-qwqikw6                           py-sympy/1.11.1-gqgr7wu                     zlib/1.2.13-jv5y5e7
   fastx-toolkit/0.0.14-zhaxiyn                     liggghts/3.8.0s-rqph5mk                        pycharm-community/2021.3.3-weqrcly          zoxide/0.9.2-ydhigq6
   ferret/7.6.0-i6u5m7q                             linaro-forge/23.1.2-pk2lobu                    pypy/7.3.13-6a5ma5j                         zstd/1.5.5-zokfqsc
   ffmpeg/6.0-fy677gn                               llvm/16.0.2-mq6g5lb                            python/3.9.16s-x3wdtvt
   ffmpeg/7.0-xny2fb2                        (D)    lmod/8.7.24-w2akdkb                            python/3.11.0s-ixrhc3q               (D)
   fiji/20231107-1617-espdc7g                       macaulay2-container/1.2-vupc5gy                qgis/3.28.3-5axmqsj

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 to load the latest version of matlab (R2023a). Other versions can be specified with the command module load matlab/R2019a.

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.

1. Open the Terminal and connect to Oscar.

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. 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. Command module avail mathematica to list all the available Mathematica versions.

4. Command module load mathematica to load the latest Mathematica version.

5. Command mathematica to launch the Mathematica app.

Oscar Software Modules

Oscar: R

Loading and Launching R

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

3. To load R, use module load r/4.4.2-re5rjx3, or another version of your preference.

4. Once inside the container's shell, use R to launch the R console.

Installing R packages

1. R packages need to be installed locally.

2. First, load the R version that you want to use the package with: module load r/4.2.2

3. Start R Session R

4. For packages that require code to be complied, install them in the log in node. Refer to more information here

5. To reinstall packages, start a new session by:

module load r/4.2.2
R

6. Then, update packages with update.packages(checkBuilt=TRUE, ask=FALSE)

7. To uninstall packages, start an R session. Then run remove.packages("wordcloud")(word cloud can be replaced with string of another package name)

Oscar Software Modules

Oscar: Macaulay 2

Loading and launching Macaulay 2

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

1. 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.

2. The Macaulay 2 module provides containers. To load them, use module load macaulay2-container/1.2-vupc5g

3. To start the container use apptainer shell $MACAULAY2_CONTAINER

4. Once inside the container's shell, use M2 to launch the Macaulay console.

Oscar Software Modules

Oscar: Magma

Loading and launching Magma

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

2. Magma requires a GPU partition. Start an interactive node by commanding interact -q gpu".

3. To load Magma, use module load magma-usyd/V2.28-8-p3zylpg.

4. Use magma to launch the Magma console.