Dedalus is a framework for solving partial differential equations using pseudo-spectral methods. The code is open source and has historically been very well maintained by an active community of researchers. Dedalus is written primarily in Python and uses friendly symbolic equation entry for model design which allows for fast prototyping and design. It is parallelized for MPI.

Individuals interested in understanding spectral and pseudospectral methods in more detail should consult "Chebyshev and Fourier Spectral Methods" by J.P. Boyd, and "Spectral Methods in MATLAB" by Lloyd N. Trefethen. The documentation provided by the Dedalus team is also a good first resource for understanding how to use the package.


As of writing, DedalusV2 is the primary version and can be installed using this dedalus-spec.txt file.

Download that file and copy it to your account on any MFCF Linux research server, then run these commands:

  • module load anaconda
  • conda env create --name dedalusv2 --file ~/dedalus-spec.txt
  • conda activate dedalusv2
  • conda env config vars set OMP_NUM_THREADS=1

  • conda env config vars set NUMEXPR_MAX_THREADS=1

Verify the Anaconda environment installation by running python -m dedalus test. There should be no failed tests; however, 'xfailed' and 'warning' are fine.

At this point you are ready to begin exploring Dedalus. You are also free to modify the Anaconda environment as you see fit by installing other Python packages using conda. In the event that some packages conflict with Dedalus, or things stop working for some other reason, you can uninstall them individually, or you can completely rebuild the environment by running

  • conda deactivate
  • conda env remove --name dedalusv2

before repeating the installation steps.

Using the environment

  • ensure anaconda module is loaded: module load anaconda
  • activate dedalusv2 environment: conda activate dedalusv2
  • your shell prompt should now look like this, for your userid and whichever CPU server you are on: (dedalusv2) userid@cpuNNN
  • to exit the dedalusv2 environment: conda deactivate


The following example files show an example procedure for completing a full problem in Dedalus. They show how to solve a non-linear initial value problem with or without multithreading, store the results using the HDF5 file format commonly used in high performance computing, as well as construct a virtual dataset. Finally, the virtual dataset is used to generate a couple of figures.

Getting help

Dedalus utilizes h5py to store long simulations. The HDF5 data format lets you store large amounts of data and manipulate it utilizing NumPy-style syntax. Becoming familiar with this framework through their examples will help with long simulations.