Running

Cooldown

cooldown.py

We provide a python script that generates directories, input files and batch scripts for a cooldown. By default, this script and the provided template input files are configured for DCA⁽⁺⁾ calculations of the 2D single-band Hubbard model with on-site Coulomb interaction U and fixed density d.

Usage

1. Create a work directory that contains

   • the script cooldown.py,
   • the input templates input_sp.json.in and input_tp.json.in,
   • and the binaries main_dca and main_analysis.

2. Configure input_sp.json.in and input_tp.json.in:

   • Provide the parameter groups for your selected build options (cluster solver and lattice model).
   • Set the simulation parameters that do not contain PLACEHOLDERS (capitalized string values), which will be substituted by cooldown.py.
   • If threaded Monte Carlo integration has been enabled, choose an appropriate number of walker and accumulator threads according to your system's specification.

3. Create a batch script template in your work directory.
   The file should contain the placeholder JOBS, which will be substituted with the schedule of dca and analysis runs for the cooldown (see example above). In addition, the following placeholders can be used to specify, for example, the job name or the standard error and output files:

   • APPLICATION: dca | analysis
   • HUBBARDU: value of the on-site Coulomb interaction U
   • DENS: value of the density d
   • SIZE: DCA cluster size

   We proivde batch script examples for SLURM (GPU, multi-core), PBS and local machines.

4. Configure the EDIT-block of cooldown.py:

   • batch_tmpl: Filename (including path) of your batch script template.
   • run_command_dca: Command (including options) to run (MPI) programs, e.g. 'mpirun -n 8'. Used to execute main_dca.
   • run_command_analysis (default value: run_command_dca): The run command to execute main_analysis. It might differ from run_command_dca, if the system's run command contains resources explicitely (e.g. aprun -n 20 -N 2 -d 8). The analysis runs usuallly require much less resources than the dca runs.
   • Simulation parameters.

5. Execute the python script:

    python cooldown.py
   

6. Revise the generated batch scripts for the dca (job.dca_*) and analysis (job.analysis_*) jobs and adjust resources if necessary.

7. Submit the dca batch script, job.dca_*.

8. When all dca jobs are finished, submit the analysis batch script, job.analysis_*.