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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} |
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We can also make an image from data:
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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' |
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Key lines from the results:
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From cProfile we 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