Description

This pull request splits the computation up in three stages:

1. Preprocessor functions are run in parallel using Dask without saving data
2. Preprocessor files are populated with data in parallel using Dask
3. Diagnostic scripts are run

Only works with max_parallel_tasks: 1 at the moment.

Ideas for further improvements:

1. optimize multi-model functions, as these limit parallelism
2. use one delayed per group in multi-model/ensemble means to increase parallelism
3. try to make delayed operations 'pure', e.g. by copying the input cubes in preprocess before calling the preprocessor function
4. see if splitting Dataset.load prior to preprocessor step concatenate up in multiple delayeds improves parallelism

Blocking issues

These are things that block this from being used in practice.

1. ESMPy crashes if you try to from a different thread than the main one. Example script that produces the crash:

   import threading
   
   import numpy as np
   
   
   def run():
       import esmpy
       m = esmpy.Manager(debug=True)
       esmpy.Grid(np.array((10, 20)),
                  num_peri_dims=1,
                  staggerloc=[esmpy.StaggerLoc.CENTER])
   
   
   def main():
   
       thread = threading.Thread(target=run)
       thread.start()
       thread.join()
   
   
   if __name__ == '__main__':
       main()

   results in Segmentation fault (core dumped) and a log file called PET0.ESMF_LogFile is written by ESMF with the following content:

   20240226 150217.785 INFO             PET0 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
   20240226 150217.785 INFO             PET0 !!! THE ESMF_LOG IS SET TO OUTPUT ALL LOG MESSAGES !!!
   20240226 150217.785 INFO             PET0 !!!     THIS MAY CAUSE SLOWDOWN IN PERFORMANCE     !!!
   20240226 150217.785 INFO             PET0 !!! FOR PRODUCTION RUNS, USE:                      !!!
   20240226 150217.785 INFO             PET0 !!!                   ESMF_LOGKIND_Multi_On_Error  !!!
   20240226 150217.785 INFO             PET0 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
   20240226 150217.785 INFO             PET0 Running with ESMF Version   : 8.4.2
   20240226 150217.785 INFO             PET0 ESMF library build date/time: "Apr 26 2023" "11:27:56"
   20240226 150217.785 INFO             PET0 ESMF library build location : /home/conda/feedstock_root/build_artifacts/esmf_1682507633250/work
   20240226 150217.785 INFO             PET0 ESMF_COMM                   : mpiuni
   20240226 150217.785 INFO             PET0 ESMF_MOAB                   : enabled
   20240226 150217.785 INFO             PET0 ESMF_LAPACK                 : enabled
   20240226 150217.785 INFO             PET0 ESMF_NETCDF                 : enabled
   20240226 150217.785 INFO             PET0 ESMF_PNETCDF                : disabled
   20240226 150217.785 INFO             PET0 ESMF_PIO                    : disabled
   20240226 150217.785 INFO             PET0 ESMF_YAMLCPP                : enabled
   20240226 150217.785 ERROR            PET0 ESMCI_VM.C:2169 ESMCI::VM::getCurrent() Internal error: Bad condition  - - Could not determine current VM
   

   Issue reported via ESMF support mailinglist

Concerns

These are things that we need to be careful about, but should not a problem.

1. thread safety, known unsafe libraries:
   • NetCDF4 library
2. custom configuration (config-developer, extra facets, custom cmor tables) may not be available on Dask workers
3. is provenance correctly updated with results from preprocessing before saving?
4. potential for re-using parts of the computation seems limited da.store loses dependency information dask/dask#8380

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Checklist

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• 🛠 This pull request has a descriptive title and labels
• 🛠 Code is written according to the code quality guidelines
• 🧪 and 🛠 Documentation is available
• 🛠 Unit tests have been added
• 🛠 Changes are backward compatible
• 🛠 Any changed dependencies have been added or removed correctly
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