The Julia codes are parallelized on the available cores. In some cases we adopted a hybrid multilevel parallelization scheme, exploiting the available processes, threads and loop vectorization. It is therefore advisable for the performance to use a number of workers * threads equal to or less than the physical number of cores present on the system.
A full list of the employed Julia packages can be found in ./julia_codes/pkgs.jl. Before executing the codes, all packages must be installed.
The Julia's Just-in-Time compiler is such that the first execution of functions is considerably slower that following ones, and it also allocates much more memory. To avoid this, you can use the DaemonMode package.
The ipe file diagrams_code_notation.ipe contains the explicit structure of the spinfoams diagrams. The spins labels exactly match the ones implemented in the Julia scripts. We changed the names of the intertwiners to improve the presentation on the paper. We provide the computed data in the data folder.
We provide the Mathematica notebooks used for the data analysis in the Notebooks folder.
To execute the Julia codes (on a single machine with the synthax below) you can run the following command:
$JULIA_EXECUTABLE_PATH -p [N-1] --threads [T] $JULIA_CODE_PATH $ARGS
where [N-1] is the number of workers and [T] is the number of threads. For an explicit example, see the CC_script.sh file, which was used to run the codes on Compute Canada's Graham cluster.