Profilers

I use two profilers:

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

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

Requirements

python 3.10: module load itm-python/3.10

line-profiler : pip install line-profiler

gprof2dot : pip install gprof2dot

How to run?

Prepare a standard launch
Copy python directory from /gss_efgw_work/work/g2fjc/jintrac/v220922/python to some location on pfs for example: /pfs/work/g2pbloch/python (I use paths with my username, change them to ones with your username)
Change JINTRAC_PYTHON_DIR in rjettov (430 line) to python directory on pfs from step 2
Change run_python_driver (line 49) in /pfs/work/g2pbloch/python (I use paths with my username, change them to ones with your username):
cProfile : mpirun --allow-run-as-root -np $NPROC python -u -m cProfile -o jintrac.prof /pfs/work/g2pbloch/python/jintrac_imas_driver.py mpi
line-profiler : mpirun --allow-run-as-root -np $NPROC python -u -m kernprof -l /pfs/work/g2pbloch/python/jintrac_imas_driver.py mpi
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 :
Code Block
language py
title function wrapper
@profile def jintrac_imas_driver(params, components, mpi='no'): """JINTRAC-IMAS generic workflow driver."""
Run ./rjettov -S -I -p -x64 test v220922 g2fjc

cProfile analysis

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

Code Block

language	py
title	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 result:

Code Block

   Wed Nov 30 09:40:33 2022    test.prof

         19379745 function calls (19376063 primitive calls) in 164.514 seconds

   Ordered by: internal time

   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}

Complete file is below:

View file

name	cProfile_result.txt
height	250

We can also make an image from data:

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

Example image looks like this ( output.svg is simplified image, output1.svg is complete image with full data):

View file

name	output.svg
height	250

View file

name	output1.svg
height	250

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 :

Code Block

title	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'

Complete file is below:

View file

name	line_profiler_result.txt
height	250

Key lines from the results:

Code Block

title	Key lines from line-profiler

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))

Candidates for bottlenecks

From cProfile result can see that 85% execution time jintrac_imas_driver.py spend on imas functions. Function ual_open_pulse was called only 3 times. It execution time is 27s. Functions ual_read_data_array and ual_read_data_scalar was called more than 2 milions times.

From line-profiler we can see, that jintrac_imas_driver.py spends a lot of time in lins with functioin bundle_copy. This function call imas functions. A lot of time is also taken up by functions calls with DBentry.

Possible ways to speed up jintrac_imas_driver.py:

Reducing number of calls: ual_open_pulse, ual_read_data_array, ual_read_data_scalar

Summary of July Profiling (HFD5)

Instruction

Code Block

title	JINTRAC version to use with HDF5 (latest AL) on GW

module use /pfs/work/g2fjc/jintrac/v120523/modules
module load jintrac
export JINTRAC_IMAS_BACKEND=HDF5
module use /pfs/g2fjc/cmg/jams/v230123/modules
module load jams
(Create test case data)
idscp -u g2pmanas --database west -si 54762 -so 54762 -ri 123 -ro 123 -bo HDF5
(path)
/pfs/work/g2fjc/jetto/runs/runmemleak_v120523_np1_z0_1ms

Line Profiler

Key lines from the results:

Code Block

title	Key lines from the results

Timer unit: 1e-06 s
Total time: 155.404 s
File: /pfs/work/g2pbloch/jintrac/python/jintrac_imas_driver.py
Function: jintrac_imas_driver at line 735

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   957        22   44253622.5 2011528.3     28.5           ids_bundle_input[ids_struct] = eval('DBentry.idsin.get("'+ids_struct+'")')
   962         1   46906374.5 46906374.5     30.2          tmpdict = bundle_copy(ids_bundle_input)
   996         2    5116586.7 2558293.3      3.3           ids_bundle_input[elem] = DBentry.idsin.get_slice(elem, tstart, interp_value)
  1011         1    3447098.9 3447098.9      2.2           ids_bundle_work = bundle_copy(ids_bundle_input)
  1012         1    4400338.9 4400338.9      2.8           ids_bundle_updated = bundle_copy(ids_bundle_input)
  1067         3    6886544.4 2295514.8      4.4           ids_bundle_prev[item] = bundle_copy(ids_bundle_work,imas_control.get_ids_sublist_updates(item))
  1099         3   29669430.5 9889810.2     19.1           ids_bundle_updated_tmp = eval(imas_control.get_wrapper(item) + args)
  1105        11    4863627.8 442148.0      3.1            ids_bundle_updated[ids_struct].copyValues(ids_bundle_updated_tmp[ids_struct])
  1106        11    3643527.1 331229.7      2.3            ids_bundle_prev[item][ids_struct].copyValues(ids_bundle_updated_tmp[ids_struct])
  1114         1    4798639.4 4798639.4      3.1           ids_bundle_work = bundle_copy(ids_bundle_updated)
  

Total time: 12.4112 s
File: /pfs/work/g2pbloch/jintrac/python/jintrac_imas_driver.py
Function: jetto_wrapper at line 304
Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   323         1          0.7      0.7      0.0      out_prof, out_src, out_trans, out_equil, out_summ, out_num, out_work \
   324         1    8768238.1 8768238.1     70.6                = jetto(in_prof, in_src, in_trans, in_equil, in_nbi,
   325         1          0.3      0.3      0.0                        in_pel, in_pulse, in_contr, in_summ, in_num, in_work)
   367         1    3589306.4 3589306.4     28.9      out_ids_bundle = bundle_copy(in_ids_bundle_work)

Complete file is below:

View file

name	line_profiler_result.txt
height	250

cProfiler

Code Block

title	Key lines from the results

Wed Jul  5 09:31:02 2023    jintrac.prof

         207387232 function calls (178651380 primitive calls) in 151.148 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
26076870/927   40.102    0.000   82.859    0.089 copy.py:128(deepcopy)
  3356451   24.272    0.000   36.639    0.000 {imas_3_38_1_ual_4_11_4._ual_lowlevel.ual_read_data_array}
778014/927    9.056    0.000   82.841    0.089 copy.py:227(_deepcopy_dict)
 53709341    7.111    0.000    7.111    0.000 {method 'get' of 'dict' objects}
        1    5.916    5.916    7.367    7.367 wrapper.py:48(jetto_actor)
  2959395    4.441    0.000    4.441    0.000 {imas_3_38_1_ual_4_11_4._ual_lowlevel.ual_read_data_scalar}
  4441187    4.234    0.000    4.234    0.000 {method '__deepcopy__' of 'numpy.ndarray' objects}
1556028/927    4.081    0.000   82.851    0.089 copy.py:259(_reconstruct)
  7099231    3.948    0.000    5.721    0.000 copy.py:243(_keep_alive)
 35834998    3.774    0.000    3.774    0.000 {built-in method builtins.id}
  3398693    3.110    0.000    3.110    0.000 {built-in method numpy.zeros}
  1005851    3.045    0.000    3.045    0.000 {method 'reduce' of 'numpy.ufunc' objects}
 18974180    2.106    0.000    2.106    0.000 copy.py:182(_deepcopy_atomic)
  1556028    1.918    0.000    1.921    0.000 {method '__reduce_ex__' of 'object' objects}
  8578377    1.660    0.000    1.660    0.000 {built-in method builtins.getattr}
   134159    1.607    0.000    1.607    0.000 {imas_3_38_1_ual_4_11_4._ual_lowlevel.ual_begin_arraystruct_action}
   509900    1.504    0.000    3.231    0.000 fromnumeric.py:38(_wrapit)
  1005853    1.383    0.000   10.248    0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
        1    1.212    1.212  151.202  151.202 jintrac_imas_driver.py:1(<module>)
  1274146    1.140    0.000    1.140    0.000 {method 'items' of 'dict' objects}
   495951    1.116    0.000    3.696    0.000 fromnumeric.py:69(_wrapreduction)
   509900    1.051    0.000    1.051    0.000 {method 'reshape' of 'numpy.ndarray' objects}

Complete file is below:

View file

name	cprofile_result.txt
height	250

Possible bottlenecks

Process of copying data ( bundle_copy , copyValues ).
eval(imas_control.get_wrapper(item) + args) and eval(imas_control.get_wrapper(item) + args) take almost half of function time

Testing HDF5

HDF5_BACKEND_READ_CACHE - TABLE

Markdown

|HDF5_BACKEND_READ_CACHE [MB]   |jintrac_imas_driver |jetto_wrapper |jintrac_imas_driver |jetto_wrapper|
|getslice interpolation| off | off| on |on |
|----------|------------|------------|-----------|-------------|
|   4 | 241.739 | 12.9481 | 230.008 | 12.136  |
|   8 | 234.774 | 12.3213 | 228.837 | 12.1292 |
|  12 | 168.333 | 13.0729 | 148.889 | 12.0893 |
|  16 | 154.656 | 12.3905 | 152.87  | 12.3117 |
|  20 | 152.988 | 12.4138 | 155.945 | 12.4595 |
|  24 | 150.913 | 12.283  | 155.469 | 12.7765 |
|  28 | 153.991 | 12.5123 | 153.11  | 12.3468 |
|  32 | 153.903 | 12.4356 | 149.575 | 12.1548 |
|  36 | 159.225 | 12.6422 | 151.492 | 12.1888 |
|  40 | 149.549 | 12.2838 | 152.926 | 12.207  |
|  44 | 158.385 | 12.5754 | 154.827 | 12.266  |
|  48 | 153.306 | 12.3843 | 152.786 | 12.3101 |
|  52 | 150.771 | 12.3464 | 151.305 | 12.1539 |
|  56 | 165.846 | 13.0865 | 153.383 | 12.2905 |
|  60 | 153.729 | 12.6542 | 155.563 | 12.4427 |
|  64 | 161.231 | 12.5552 | 151.229 | 12.2685 |
|  68 | 154.142 | 12.4402 | 148.697 | 12.0926 |
|  72 | 153.52  | 12.4294 | 149.757 | 12.1072 |
|  76 | 149.982 | 12.3277 | 151.765 | 12.2017 |
|  80 | 155.473 | 12.6245 | 151.903 | 12.2647 |
|  84 | 150.118 | 12.2872 | 152.894 | 12.2489 |
|  88 | 149.918 | 12.2017 | 154.456 | 12.285  |
|  92 | 151.403 | 12.317  | 149.207 | 12.0798 |
|  96 | 150.825 | 12.3905 | 152.792 | 12.2197 |
| 100 | 149.834 | 12.6997 | 147.859 | 12.0551 |
| 104 | 150.326 | 12.3421 | 148.031 | 12.062  |
| 108 | 151.51  | 12.3496 | 152.049 | 12.2785 |
| 112 | 155.506 | 12.4702 | 154.526 | 12.3407 |
| 116 | 153.662 | 12.4152 | 153.498 | 12.2769 |
| 120 | 158.311 | 12.7339 | 148.607 | 12.1153 |
| 124 | 154.864 | 12.2361 | 156.592 | 12.5044 |
| 128 | 153.5   | 12.2201 | 154.231 | 13.1112 |
| 132 | 152.191 | 12.3723 | 156.739 | 12.4632 |
| 136 | 160.911 | 12.5815 | 152.368 | 12.3718 |
| 140 | 159.165 | 12.8561 | 149.531 | 12.1498 |
| 144 | 153.646 | 12.4145 | 151.239 | 12.1133 |
| 148 | 149.434 | 12.3417 | 147.887 | 11.9921 |
| 152 | 151.505 | 12.3484 | 151.848 | 12.4215 |
| 156 | 151.068 | 12.3288 | 156.421 | 12.4458 |
| 160 | 152.951 | 12.4558 | 155.213 | 12.4617 |
| 164 | 149.566 | 12.2848 | 151.589 | 12.2744 |
| 168 | 151.872 | 12.4422 | 151.654 | 12.4456 |
| 172 | 152.766 | 12.4294 | 153.34  | 12.2264 |
| 176 | 151.982 | 12.4162 | 149.911 | 12.254  |
| 180 | 152.768 | 12.4248 | 156.015 | 12.3302 |
| 184 | 148.166 | 12.2076 | 149.323 | 12.2971 |
| 188 | 153.493 | 12.4229 | 153.415 | 13.0557 |
| 192 | 153.968 | 12.3694 | 157.029 | 12.4348 |
| 196 | 152.465 | 12.3764 | 154.288 | 12.2881 |
| 200 | 149.238 | 12.2266 | 154.713 | 12.3013 |

HDF5_BACKEND_READ_CACHE - PLOTS

Image ModifiedImage ModifiedImage ModifiedImage Modified

TESTING MDSPLUS

LINE PROFILER

Code Block

title	Line profiler

Timer unit: 1e-06 s

Total time: 155.743 s
File: /pfs/work/g2pbloch/jintrac/python/jintrac_imas_driver.py
Function: jintrac_imas_driver at line 735

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   896         1    1638364.1 1638364.1      1.1                  status, idx_out = DBentry.idsout.create()  # idx_out points to output IDS file index
   957        22   50742304.5 2306468.4     32.6                      ids_bundle_input[ids_struct] = eval('DBentry.idsin.get("'+ids_struct+'")')
   962         1   45878227.2 45878227.2     29.5          tmpdict = bundle_copy(ids_bundle_input)
   996         2    3193050.2 1596525.1      2.1                              ids_bundle_input[elem] = DBentry.idsin.get_slice(elem, tstart, interp_value)
  1011         1    3399663.8 3399663.8      2.2      ids_bundle_work = bundle_copy(ids_bundle_input)
  1012         1    4340984.3 4340984.3      2.8      ids_bundle_updated = bundle_copy(ids_bundle_input)
  1067         3    6779104.9 2259701.6      4.4          ids_bundle_prev[item] = bundle_copy(ids_bundle_work,imas_control.get_ids_sublist_updates(item))
  1199         3   25454536.5 8484845.5     16.3                  ids_bundle_updated_tmp = eval(imas_control.get_wrapper(item) + args)
  1105        11    4817487.6 437953.4      3.1                              ids_bundle_updated[ids_struct].copyValues(ids_bundle_updated_tmp[ids_struct])
  1106        11    3587909.3 326173.6      2.3                              ids_bundle_prev[item][ids_struct].copyValues(ids_bundle_updated_tmp[ids_struct])
  1114         1    4769085.3 4769085.3      3.1          ids_bundle_work = bundle_copy(ids_bundle_updated)

Total time: 12.4592 s
File: /pfs/work/g2pbloch/jintrac/python/jintrac_imas_driver.py
Function: jetto_wrapper at line 304

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
   323         1          1.3      1.3      0.0      out_prof, out_src, out_trans, out_equil, out_summ, out_num, out_work \
   324         1    8958798.8 8958798.8     71.9                = jetto(in_prof, in_src, in_trans, in_equil, in_nbi,
   325         1          0.2      0.2      0.0                        in_pel, in_pulse, in_contr, in_summ, in_num, in_work)
   367         1    3459717.4 3459717.4     27.8      out_ids_bundle = bundle_copy(in_ids_bundle_work)

Complete file is below:

View file

name	line_profiler_result.txt
height	250

CPROFILER

Code Block

title	Cprofiler

Thu Jul 20 14:45:51 2023    jintrac.prof

         207169007 function calls (178433155 primitive calls) in 153.082 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
26076870/927   40.886    0.000   83.306    0.090 copy.py:128(deepcopy)
  3356451   22.884    0.000   35.138    0.000 {imas_3_38_1_ual_4_11_3._ual_lowlevel.ual_read_data_array}
778014/927    8.909    0.000   83.288    0.090 copy.py:227(_deepcopy_dict)
  2959395    8.181    0.000    8.181    0.000 {imas_3_38_1_ual_4_11_3._ual_lowlevel.ual_read_data_scalar}
 53709341    6.903    0.000    6.903    0.000 {method 'get' of 'dict' objects}
        1    5.946    5.946    7.387    7.387 wrapper.py:48(jetto_actor)
  4441187    4.289    0.000    4.289    0.000 {method '__deepcopy__' of 'numpy.ndarray' objects}
1556028/927    4.099    0.000   83.298    0.090 copy.py:259(_reconstruct)
  7099231    3.986    0.000    5.677    0.000 copy.py:243(_keep_alive)
 35834998    3.833    0.000    3.833    0.000 {built-in method builtins.id}
  3398693    2.945    0.000    2.945    0.000 {built-in method numpy.zeros}
   987875    2.815    0.000    2.815    0.000 {method 'reduce' of 'numpy.ufunc' objects}
 18974180    2.163    0.000    2.163    0.000 copy.py:182(_deepcopy_atomic)
  1556028    1.704    0.000    1.707    0.000 {method '__reduce_ex__' of 'object' objects}
  8542427    1.672    0.000    1.672    0.000 {built-in method builtins.getattr}
   495951    1.465    0.000    4.334    0.000 fromnumeric.py:69(_wrapreduction)
  1274146    1.441    0.000    1.441    0.000 {method 'items' of 'dict' objects}
   491924    1.422    0.000    3.040    0.000 fromnumeric.py:38(_wrapit)
        1    1.277    1.277  153.141  153.141 jintrac_imas_driver.py:1(<module>)
   987877    1.196    0.000   10.454    0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
   491924    1.001    0.000    1.001    0.000 {method 'reshape' of 'numpy.ndarray' objects}

Complete file is below:

View file

name	cprofile_result.txt
height	250

Partial_get vs get

IDS :: Core_profiles

The test is checking the time differences between fetching whole IDS using get and fetching IDS in parts using partial_get .

Code Block

language	py
title	source code of patial_get vs get test

import sys
import numpy
import os
import time
import imas

user = os.environ['USER']
imasVersion = os.environ['IMAS_VERSION'][:1]
database = 'test'
backendtype = 'HDF5'
machine_in = "west"

ids_in = imas.DBEntry(13, "west", 54762, 1, user_name="/pfs/work/g2pbloch/jetto/runs/test/imasdb")
ids_in.open()

start_partial = time.time()
rho_tor_norm = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/grid/rho_tor_norm(:)")
core_profiles_profiles_1d_zeff = ids_in.partial_get("core_profiles", "profiles_1d(:)/zeff(:)")
rho_tor_norm = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/grid/rho_tor_norm(:)")
q = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/q(:)")
density_thermal = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/electrons.density_thermal(:)")
temperature = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/electrons/temperature(:)")
ion = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/ion")
t_i_average = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/t_i_average(:)")
zeff = ids_in.partial_get("core_profiles" ,"profiles_1d(:)/zeff(:)")
rotation_frequency_tor_sonic = ids_in.partial_get("core_profiles", "profiles_1d(:)/rotation_frequency_tor_sonic")
end_partial = time.time()

start_get = time.time()
core_profiles = ids_in.get("core_profiles")
end_get = time.time()

print(f"partial_get: {end_partial - start_partial}, get : { end_get - start_get}")
ids_in.close()

The time gain is small, just below 1 second, for the entire duration of about 24 seconds:

partial_get: 23.91968011856079, get : 24.872377157211304

IDS :: equilibrium

Testing description

I couldn't retrieve the data using, for example: equilibriumtime_sliceitimeglobal_quantitiesip = ids_in.partial_get("equilibrium", "time_slice(:)/global_quantities/ip") because I was getting the following error: ERROR: [time_slice] AoS internal field(s) cannot be returned in non-homogeneous mode for more than one AoS index . Due to this, I had to iterate and retrieve the data for each time separately.

When it comes to get_slice , I retrieve timestamps using partial_get . Then, for each timestamp, I fetch a segment using get_slice and apply imas.imasdef.LINEAR_INTERP interpolation.

Code Block

language	py
title	partial_get vs get

import sys
import numpy
import os
import time
import imas
def run():	
	user = os.environ['USER']
	imasVersion = os.environ['IMAS_VERSION'][:1]
	database = 'test'
	backendtype = 'HDF5'
	machine_in = "west"
	ids_in = imas.DBEntry(13, "west", 54762, 1, user_name="/pfs/work/g2pbloch/jetto/runs/test/imasdb")
	ids_in.open()
	start_partial = time.time()
	eq_time = ids_in.partial_get("equilibrium", "time(:)")
	equilibriumtime_sliceitimeglobal_quantitiesip  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/ip") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiespsi_axis  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/psi_axis") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiespsi_boundary  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/psi_boundary") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiesmagnetic_axisr  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/magnetic_axis/r") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiesmagnetic_axisz  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/magnetic_axis/z") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_1drho_tor_norm  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_1d/rho_tor_norm(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_1dpsi  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_1d/psi(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeboundaryoutliner  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/boundary/outline/r(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeboundarylcfsr  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/boundary/lcfs/r(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeboundaryoutlinez  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/boundary/outline/z(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeboundarylcfsz  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/boundary/lcfs/z(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_2d1psi  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_2d(1)/psi(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_2d1griddim1  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_2d(1)/grid/dim1(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_2d1griddim2  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_2d(1)/grid/dim2(:)") for i in range(len(eq_time))]
	end_partial = time.time()
	
	start_get = time.time()
	equilibrium = ids_in.get("equilibrium")
	end_get = time.time()
	print(f"partial_get: {end_partial - start_partial}, get : {end_get - start_get}")
	ids_in.close()
if __name__ == "__main__":
	run()

Code Block

language	py
title	get_slice

import sys
import numpy
import os
import time
import imas

def run():	
	user = os.environ['USER']
	imasVersion = os.environ['IMAS_VERSION'][:1]
	database = 'test'
	backendtype = 'HDF5'
	machine_in = "west"
	ids_in = imas.DBEntry(13, "west", 54762, 1, user_name="/pfs/work/g2pbloch/jetto/runs/test/imasdb")
	ids_in.open()
	eq_time = ids_in.partial_get("equilibrium", "time(:)")
	get_slice_start = time.time()
	data = [ids_in.get_slice("equilibrium", t, imas.imasdef.LINEAR_INTERP) for t in eq_time]
	get_slice_end = time.time()
	print(get_slice_end-get_slice_start)
if __name__ == "__main__":
	run()

Time result

partial_get without profiles_2d : 4.72s
partial_get with profiles_2d : 9.682.682 ( profiles_2d(1)/psi: 3.6s , profiles_2d(1)/grid/dim1 : 0.76s , profiles_2d(1)/grid/dim2 : 0.7s)

get : 7.47s
get_slice : 718 s

September 2024

Partial_get vs get

environment:

Code Block

language	bash
title	environment

module use /pfs/work/g2fjc/jintrac/32.0.1/modules/
module load jintrac
export JINTRAC_IMAS_BACKEND=HDF5
module use /pfs/work/g2fjc/cmg/jams/31.0.0/modules
module load jams

Timing (1D) and (1D+2D) with partial get vs full get. I am using data from /gss_efgw_work/work/g2fjc/jetto/runs/runmemleak_v120523_np1_z0_1ms_hdf5/imasdb/ .

Python code to reproduce:

Code Block

language	py
title	python code to timing

import sys
import numpy
import os
import time
import imas
def run():	
	user = os.environ['USER']
	imasVersion = os.environ['IMAS_VERSION'][:1]
	database = 'test'
	backendtype = 'HDF5'
	machine_in = "west"
	ids_in = imas.DBEntry(13, "west", 54762, 1, user_name="/pfs/work/g2pbloch/jetto/runs/test/imasdb")
	ids_in.open()
	start_partial = time.time()
	eq_time = ids_in.partial_get("equilibrium", "time(:)")
	equilibriumtime_sliceitimeglobal_quantitiesip  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/ip") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiespsi_axis  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/psi_axis") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiespsi_boundary  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/psi_boundary") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiesmagnetic_axisr  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/magnetic_axis/r") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeglobal_quantitiesmagnetic_axisz  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/global_quantities/magnetic_axis/z") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_1drho_tor_norm  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_1d/rho_tor_norm(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_1dpsi  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_1d/psi(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeboundaryoutliner  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/boundary/outline/r(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeboundaryoutlinez  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/boundary/outline/z(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_2d1psi  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_2d(1)/psi(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_2d1griddim1  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_2d(1)/grid/dim1(:)") for i in range(len(eq_time))]
	equilibriumtime_sliceitimeprofiles_2d1griddim2  =  [ids_in.partial_get("equilibrium", f"time_slice({i})/profiles_2d(1)/grid/dim2(:)") for i in range(len(eq_time))]
	end_partial = time.time()
	
	start_get = time.time()
	equilibrium = ids_in.get("equilibrium")
	end_get = time.time()
	print(f"partial_get: {end_partial - start_partial}, get : {end_get - start_get}")
	ids_in.close()
if __name__ == "__main__":
	run()

Time result 1D(average of 10 measurements):

partial_get: 3.70s
full get: 7.15s

Time result 1D+2D(average of 10 measurements):

partial_get: 8.69s
full get: 7.17s

The scientific work is published for the realization of the international project co-financed by Polish Ministry of Science and Higher Education in 2019 from financial resources of the program entitled "PMW"; Agreement No. 5040/H2020/Euratom/2019/2
This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2020 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission or ITER

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Table of Contents

Profilers

Requirements

How to run?

cProfile analysis

Line-profiler analysis

Candidates for bottlenecks

Summary of July Profiling (HFD5)

Instruction

Line Profiler

cProfiler

Possible bottlenecks

Testing HDF5

HDF5_BACKEND_READ_CACHE - TABLE

HDF5_BACKEND_READ_CACHE - PLOTS

TESTING MDSPLUS

LINE PROFILER

CPROFILER

Partial_get vs get

IDS :: Core_profiles

IDS :: equilibrium

Testing description

Time result

September 2024

Partial_get vs get

Time result 1D(average of 10 measurements):

Time result 1D+2D(average of 10 measurements):

Space shortcuts

Page tree

Page History

Versions Compared

Old Version 30

New Version Current

Key

Table of Contents

Profilers

Requirements

How to run?

cProfile analysis

Line-profiler analysis

Candidates for bottlenecks

Summary of July Profiling (HFD5)

Instruction

Line Profiler

cProfiler

Possible bottlenecks

Testing HDF5

HDF5_BACKEND_READ_CACHE - TABLE

HDF5_BACKEND_READ_CACHE - PLOTS

TESTING MDSPLUS

LINE PROFILER

CPROFILER

Partial_get vs get

IDS :: Core_profiles

IDS :: equilibrium

Testing description

Time result

September 2024

Partial_get vs get

Time result 1D(average of 10 measurements):

Time result 1D+2D(average of 10 measurements):