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1. Table of Contents

2. Profilers

I use two profilers:

cProfile - https://docs.python.org/3/library/profile.html

line-profiler - https://pypi.org/project/line-profiler/

3. requirements

python   3.10   module load itm-python/3.10 

line-profiler   tapip install line-profiler 

4. How to run?

  1.  prepare a standard launch 
  2. Copy python  folder from /gss_efgw_work/work/g2fjc/jintrac/v220922/python  to some location on pfs for example: /pfs/work/g2pbloch/python 
  3. Change JINTRAC_PYTHON_DIR  in rjettov (430 line) to python folder on pfs from step 2
  4. Change run_python_driver (line 49) in /pfs/work/g2pbloch/python :
  5. cProfile :  mpirun --allow-run-as-root -np $NPROC python -u  -m cProfile -o jintrac.prof /pfs/work/g2pbloch/jetto_profiler/jintrac_imas_driver.py  mpi 
  6. line-profiler : mpirun --allow-run-as-root -np $NPROC python -u  -m kernprof -l /pfs/work/g2pbloch/jetto_profiler/jintrac_imas_driver.py  mpi 

  7. When we use line-profiler we must add wrapper to profiling function. In this case we should add @profile  upper jintrac_imas_driver function in jintrac_imas_driver.py  : 

    function wrapper
    @profile
def jintrac_imas_driver(params, components, mpi='no'):

    """JINTRAC-IMAS generic workflow driver."""
  8. Run ./rjettov -S -I -p -x64 test v220922 g2fjc 

5. cProfile analysis

To read data from test.prof we need python script. We can use e.g.

cprofile_script.py
import pstats
stats = pstats.Stats('test.prof')
stats.strip_dirs().sort_stats(pstats.SortKey.TIME).print_stats()

This script print data sorted by tottime  column ( In tottime  column is total time spent in the given function (and excluding time made in calls to sub-functions)).  These are the first few lines of the results :

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        3   81.304   27.101   81.304   27.101 {imas_3_37_0_ual_4_11_0._ual_lowlevel.ual_open_pulse}
  2891220   42.947    0.000   52.709    0.000 {imas_3_37_0_ual_4_11_0._ual_lowlevel.ual_read_data_array}
  2497358   16.127    0.000   16.127    0.000 {imas_3_37_0_ual_4_11_0._ual_lowlevel.ual_read_data_scalar}
  2899948    2.784    0.000    2.784    0.000 {built-in method numpy.zeros}
   815194    2.391    0.000    2.391    0.000 {method 'reduce' of 'numpy.ufunc' objects}
   427578    1.225    0.000    2.589    0.000 fromnumeric.py:38(_wrapit)
   815196    1.015    0.000    7.988    0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
   387616    0.876    0.000    2.809    0.000 fromnumeric.py:69(_wrapreduction)
   427578    0.848    0.000    0.848    0.000 {method 'reshape' of 'numpy.ndarray' objects}
   427578    0.685    0.000    3.358    0.000 fromnumeric.py:51(_wrapfunc)
   387785    0.684    0.000    0.684    0.000 {method 'items' of 'dict' objects}


We can also make an image from data:

  1.  Install gprof2dot : pip install gprof2dot 
  2. run : python -m gprof2dot -f pstats test.prof | dot -Tsvg -o output.svg 

Example image looks like this:

output.svg   

6. Line-profiler analysis

To read data from jintrac_imas_driver.py.lprof  run: python -m line_profiler jintrac_imas_driver.py.lprof . These are the first few lines of the results :

line-profiler result
Timer unit: 1e-06 s

Total time: 232.892 s
File: /pfs/work/g2pbloch/python/jintrac_imas_driver.py
Function: jintrac_imas_driver at line 721

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   721                                           @profile
   722                                           def jintrac_imas_driver(params, components, mpi='no'):
   723                                           
   724                                               """JINTRAC-IMAS generic workflow driver."""
   725                                           
   726                                               # Workflow configuration parameters
   727                                           
   728         1          5.9      5.9      0.0      user_in = wf.string(params, "Input IDS user")
   729         1          2.3      2.3      0.0      machine_in = wf.string(params, "Input IDS machine")
   730         1          4.7      4.7      0.0      shot_in = wf.integer(params, "Input IDS shot")
   731         1          2.1      2.1      0.0      run_in = wf.integer(params, "Input IDS run")
   732         1          2.9      2.9      0.0      user_out = wf.string(params, "Output IDS user")
   733         1          2.6      2.6      0.0      machine_out = wf.string(params, "Output IDS machine")
   734         1          2.6      2.6      0.0      shot_out = wf.integer(params, "Output IDS shot")
   735         1          2.6      2.6      0.0      run_out = wf.integer(params, "Output IDS run")
   736         1         13.3     13.3      0.0      user_tmp = os.environ['HOME']+'/public/tempdb'


Key lines from the results:

Line #      Hits         Time  Per Hit   % Time  Line Contents
   895         1   62545203.7 62545203.7     26.9      alenv = ALEnv(user_temp=user_tmp)
   917        22   49496025.7 2249819.4     21.3                  ids_bundle_input[ids_struct] = eval('DBentry.idsin.get("'+ids_struct+'")')
   922         1   83745046.9 83745046.9     36.0      tmpdict = bundle_copy(ids_bundle_input)
   956         2    4665617.1 2332808.6      2.0                          ids_bundle_input[elem] = DBentry.idsin.get_slice(elem, tstart, 3)
   966         1    6353190.5 6353190.5      2.7      ids_bundle_work = bundle_copy(ids_bundle_input)
   967         1    7884627.8 7884627.8      3.4      ids_bundle_updated = bundle_copy(ids_bundle_input)
  1022         3   14539266.0 4846422.0      6.2          ids_bundle_prev[item] = bundle_copy(ids_bundle_work,imas_control.get_ids_sublist_updates(item))




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