# Oscar # 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](https://ccv.brown.edu/services/infrastructure/oscar/ "https://ccv.brown.edu/services/infrastructure/oscar/"). 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)](https://docs.icerm.brown.edu/books/oscar/page/ssh-access-to-oscar "SSH Access to Oscar") access and [virtual desktop (VNC)](https://docs.icerm.brown.edu/books/oscar/page/vnc-access-to-oscar "VNC Access to Oscar") 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](https://docs.icerm.brown.edu/books/oscar/page/ssh-access-to-oscar "SSH Access to Oscar") 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](https://docs.icerm.brown.edu/books/oscar/page/open-ondemand-access-to-oscar) 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](https://docs.icerm.brown.edu/books/oscar/page/vnc-access-to-oscar "VNC Access to Oscar") for information on how to get started with the VNC client. ## Available Software Oscar maintains a [large library of software packages](https://docs.icerm.brown.edu/books/oscar/page/currently-available-modules "Currently Available Modules") for use on the HPC cluster. Some of the most commonly used mathematical applications and languages available include:
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MATLAB
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Mathematica
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Sage
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Julia
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R
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Python
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Maple
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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.
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To view all available software packages, type `module avail`.
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To search the list of available packages, type `module avail `.
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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.
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To load a package into your session, type `module load `.
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For example, `module load mathematica/11.0`. This will load the Mathematica 11.0 into your session and make it available for use.
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To unload a package you are no longer using, type `module unload `
### Module-specific Guides
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[Oscar: Mathematica](https://docs.icerm.brown.edu/books/oscar/page/oscar-mathematica "Oscar: Mathematica")
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[Oscar: MATLAB](https://docs.icerm.brown.edu/books/oscar/page/oscar-matlab "Oscar: MATLAB")
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[Oscar: Sage](https://docs.icerm.brown.edu/books/oscar/page/oscar-sage "Oscar: Sage")
## Running Jobs Oscar supports two main methods of running jobs: **batch jobs** and **interactive sessions**.
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Batch jobs are pre-scripted and can be submitted to the cluster's scheduler via the `sbatch` command.
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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](https://docs.ccv.brown.edu/oscar/submitting-jobs/shared-machine "https://docs.ccv.brown.edu/oscar/submitting-jobs/shared-machine") page of the [Oscar User Manual](https://docs.ccv.brown.edu/oscar/ "https://docs.ccv.brown.edu/oscar/") provides the most detailed and up-to-date instructions on scripting and submitting jobs. ## Important Notes About the Oscar Cluster
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**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`.**
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The full [Oscar User Manual](https://docs.ccv.brown.edu/oscar/ "https://docs.ccv.brown.edu/oscar/") is available on the CCV website.
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Oscar uses SLURM for managing batch jobs and interactive sessions on the cluster. Detailed instructions on submitting jobs is available on the [Running Jobs](https://docs.ccv.brown.edu/oscar/submitting-jobs/shared-machine "https://docs.ccv.brown.edu/oscar/submitting-jobs/shared-machine") page of the [Oscar User Manual](https://docs.ccv.brown.edu/oscar/ "https://docs.ccv.brown.edu/oscar/").
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CCV has a [large library of software](https://docs.icerm.brown.edu/books/oscar/page/currently-available-modules "Currently Available Modules") 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](https://docs.ccv.brown.edu/oscar/software/ "https://docs.ccv.brown.edu/oscar/software/"). 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.
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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 –user`.
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The [Oscar VNC Client](https://docs.ccv.brown.edu/oscar/connecting-to-oscar/vnc "https://docs.ccv.brown.edu/oscar/connecting-to-oscar/vnc") 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](mailto:support@icerm.brown.edu?subject=Oscar "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](https://ood.ccv.brown.edu/) 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](https://docs.icerm.brown.edu/uploads/images/gallery/2022-10/scaled-1680-/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](https://docs.ccv.brown.edu/oscar/connecting-to-oscar/open-ondemand/using-file-explorer-on-ood) [Web-based Terminal App](https://docs.ccv.brown.edu/oscar/connecting-to-oscar/open-ondemand/web-based-terminal-app) [Interactive Apps on OOD](https://docs.ccv.brown.edu/oscar/connecting-to-oscar/open-ondemand/interactive-apps-on-ood) [Using Python or Conda environments in the Jupyter App](https://docs.ccv.brown.edu/oscar/connecting-to-oscar/open-ondemand/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 @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](https://docs.ccv.brown.edu/oscar/submitting-jobs/shared-machine "https://docs.ccv.brown.edu/oscar/submitting-jobs/shared-machine") for detailed instructions on batch jobs and interactive sessions.** # Oscar Software Modules List and instructions for software modules available on the Oscar cluster. # Currently Available Modules If you require a software package that is not currently available on the Oscar cluster, please [contact ICERM's IT staff](mailto:support@icerm.brown.edu?subject=Oscar+Software+Request) 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: Sage ## Loading and Launching Sage 1\. Open the Terminal and use the following commands at the command line. 2\. `module load sage/9.0` to load Sage. This command will return confirmation “*module: loading 'sage/9.0'*”. 3\. Sage on Oscar usually has dependent modules. These will be output to your terminal when you load the Sage module similar to the example below. ```shell [username@node ~]$ module load sage/9.0 module: loading 'sage/9.0' module: sage: To use: module load gcc/8.3 ``` 4\. Load any other dependent modules for Sage that were output in the above steps. These modules must be loaded in the specific order specified by the system when the Sage module is loaded. Loading them in a different order may cause Sage to throw errors. For Sage 9.0: `module load gcc/8.3` For Sage 8.7: `module load gcc/8.3 sqlite/3.25.2 perl/5.24.1 libgd/2.2.5 mpfr/3.1.5 ffmpeg/4.0.1 imagemagick/7.0.7 texlive/2018` 5\. `sage` to launch the Sage console within your Terminal window. ## Sage on VNC The Sage install may not always work properly on Oscar's VNC nodes due to the VNC nodes running older chipsets. The easiest way to remedy this issue is to run sage in an interactive job via the terminal in your VNC 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). ```shell interact -n 2 -m 32g -t 04:00:00 -f 'haswell|broadwell|skylake' ``` ## Installing Sage Packages Locally with --user It is possible to install most Sage packages locally to your home folder by passing the `–user` parameter at the end of your install command. See below for example steps to install the Sage packages “surface\_dynamics” and “sage-flatsurf”. In this example, we are loading both packages from git repositories, so we need to load the git module. 1\. `module load git` 2\. `sage -pip install git+https://gitlab.com/videlec/surface_dynamics –user` 3\. `sage -pip install git+https://github.com/videlec/sage-flatsurf –user` ## 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:
1.
A shell script to configure and submit your batch job to the cluster.
2.
Your Sage code/program you'd like to run.
### Example Batch Script
**sage-batch.sh**
```shell #!/bin/bash #SBATCH -J test_program #SBATCH --array=0-9 #SBATCH -t 1:00:00 #SBATCH --mem=8G #SBATCH -e data//test_output/test%a.err #SBATCH -o data//test_output/test%a.out module load sage/8.7 module load gcc/8.3 sqlite/3.25.2 perl/5.24.1 libgd/2.2.5 mpfr/3.1.5 ffmpeg/4.0.1 imagemagick/7.0.7 texlive/2018 sage test_program.sage $SLURM_ARRAY_TASK_ID ```
-
`#!/bin/bash` tells the system this is a bash (shell) script.
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`#SBATCH -J test_program` sets the name of the job which appears when you check the status of your jobs.
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`#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.
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`#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.
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`#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.
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`#SBATCH -e data//test_output/test%a.err` and `#SBATCH -o data//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`.
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`module load sage/8.7` loads the sage module into the node.
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`module load gcc/8.3 sqlite/3.25.2 perl/5.24.1 libgd/2.2.5 mpfr/3.1.5 ffmpeg/4.0.1 imagemagick/7.0.7 texlive/2018` loads the dependency packages for the sage module.
Everything after this in the script happens as if you typed it yourself onto the command line.
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In our example, we want to run sage code, so the line `sage test_program.sage $SLURM_ARRAY_TASK_ID` runs our example sage program `test_program.sage`.
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The file needs to have the `.sage` extension.
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You should write this file in a text editor, not in a Jupyter notebook (although you can first write and test your program in a Jupyter notebook and then copy and paste it into a new file when it’s ready).
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This program is written to accept one input and I have passed it `$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**
```Python 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 `. 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.
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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.
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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 `. You can specify a single node or just put the general job id for the whole run and cancel everything.
# 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](https://yalmip.github.io/ "https://yalmip.github.io/"), 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.
1.
In the MATLAB file browser, navigate to the MATLAB folder you created in your home folder. `cd ~/MATLAB`
2.
Right click on the YALMIP-master folder.
3.
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](https://github.com/SQLP/SDPT3 "https://github.com/SQLP/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.
1.
In the MATLAB file browser, navigate to the MATLAB folder you created in your home folder. `cd ~/MATLAB`
2.
Right click on the sdpt3-master folder.
3.
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: 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.