The Tufts High Performance Compute (HPC) cluster delivers 35,845,920 cpu hours and 59,427,840 gpu hours of free compute time per year to the user community.

Teraflops: 60+ (60+ trillion floating point operations per second) cpu: 4000 cores gpu: 6784 cores Interconnect: 40GB low latency ethernet

For additional information, please contact Research Technology Services at tts-research@tufts.edu

SageMath

Owned by Shawn Doughty (Deactivated)
Last updated: Apr 04, 2017

SageMath is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more. Access their combined power through a common, Python-based language or directly via interfaces or wrappers. 

  1. Install your own version of python.
    login001: wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
login001: chmod u+x Miniconda3-latest-Linux-x86_64.sh
login001: ./Miniconda3-latest-Linux-x86_64.sh
  2. Install jupyter within anaconda
    login001: miniconda3/bin/conda install jupyter
  3. Start up jupyter

    1. Request a default # of cores and memory as a test.

      Get an allocation on a compute node. Write down the name of the assigned compute node
      login001: srun -c 4 --pty -p interactive bash
alpha001:

      NOTE: This gives you four cores, for future runs of jupyter you can ask for more, up to 40. Be sure to select the matching # of threads in the top right corner of the jupyter interface.

    2. Start jupyter on the compute node. It will display the port # the software is running on, usually 8888
      alpha001: miniconda3/bin/jupyter notebook --no-browser
[NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
[NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

      NOTE: Be aware of the port number 8888 above. Your port may be different, Adjust the instructions below accordingly.
      NOTE: Jupyter may output a start up token at this point which may be required when logging in via the web interface. You can cut and paste it.

  4. Access jupyter
    1. Now setup forwarding from your workstation thru the headnode to the assigned compute node, in this example it is alpha001 
      Your Workstation: ssh username@login.cluster.tufts.edu -L 8888:localhost:8888 ssh alpha001 -L 8888:localhost:8888

    2. Point the browser on your workstation to http://localhost:8888/    and the jupyter web interface should come up.

    3. NOTE: If jupyter asks for a token, this can be found when you started it up. Cut and paste the correct value.
    4. Do computation, do science!
  5. Exit jupyter
    1. Don't forget to exit jupyter so it isn't taking up resources
      The Jupyter Notebook is running at: http://localhost:8888/
Shutdown this notebook server (y/[n])? y
[C 14:19:54.199 NotebookApp] Shutdown confirmed
[I 14:19:54.199 NotebookApp] Shutting down kernels

Project Home Page

http://jupyter.org/

For additional information, please contact Research Technology Services at tts-research@tufts.edu