SpeedIT:

A Collection of: Benchmark-IT, Profile-IT, Line-Memory-Profile-IT, Disassemble-IT.

HTML documentation of the project is hosted at: SpeedIT-HTML documentation

Or Package Documentation

SpeedIT is a small collection of 4 modules: BenchmarkIT, ProfileIT, LineMemoryProfileIT, DisassembleIT and additional the combined: MainCode module

seealso: There is a newer project which outputs in html files

• PySpeedIT

MainCode.speed_it function for easy combined: <BenchmarkIT, ProfileIT, LineMemoryProfileIT, DisassembleIT>

To use it one needs to define a couple of functions to benchmark

from SpeedIT.MainCode import speed_it

test_value = '~/etc/mypath'

# define SpeedIT functions
def example_startswith():
   if test_value.startswith('~/'):
      pass

def example_two_idx():
   if test_value[0] == '~' and test_value[1] == '/':
      pass

def example_slice():
   if test_value[:2] == '~/':
      pass

This is a dictionary with key(names) and a tuple per function:

• value format: tuple (function, list_of_positional_arguments, dictionary_of_keyword_arguments)

# defining the: func_dict mapping
func_dict = {
   # value format: tuple (function, list_of_positional_arguments, dictionary_of_keyword_arguments)
   'startswith': (example_startswith, [], {}),
   'two_idx': (example_two_idx, [], {}),
   'slice': (example_slice, [], {}),
}

This is a list with all needed code to setup so that the functions can run: e.g. imports, global variables

setup_line_list = [
   'from __main__ import test_value'
]

For the available options see the API-DOC or source code

result = speed_it(
   func_dict,
   setup_line_list,
   enable_benchmarkit=True,
   enable_profileit=True,
   enable_linememoryprofileit=True,
   enable_disassembleit=True,
   use_func_name=False,
   output_in_sec=False,
   profileit__max_slashes_fileinfo=2,
   profileit__repeat=1,
   benchmarkit__with_gc=False,
   benchmarkit__check_too_fast=True,
   benchmarkit__rank_by='best',
   benchmarkit__run_sec=1,
   benchmarkit__repeat=3
)

with open('result_output/ReadmeExampleMainSpeedIT.txt', 'w') as file_:
   file_.write('\n\n ReadmeExampleMainSpeedIT.py output\n\n')
   file_.write(result)

BenchmarkIT supports also timing of only selected code parts within a function using Comment lines with a START/END TAG.

START-TAG: # ::SPEEDIT::
END-TAG:   # **SPEEDIT**

The code below will report the combined time of the code part between # ::SPEEDIT:: and # **SPEEDIT**

• in the case below skipping the time spent in shuffle(data)

def example_multiple_subcode_blocks():
   # ::SPEEDIT:: data
   data = dict(zip(range(1000), range(1000)))
   # **SPEEDIT**
   shuffle(data)
   # ::SPEEDIT:: sorted
   result = sorted(data.items(), key=itemgetter(1))
   del result
   # **SPEEDIT**

+-----------------------------------------------------------------------------------------------------------------------------------------------------------+ | SpeedIT: BenchmarkIT for: <3> functions. benchmarkit__with_gc: <False> benchmarkit__run_sec: <1> | +-------------------------+-----------+-----------+------------+-----------+-----------+------------------+------------+-------------------+----------------+ | name | rank-best | compare % | num. loops | avg_loop | best_loop | second_best_loop | worst_loop | second_worst_loop | all_loops time | +=========================+===========+===========+============+===========+===========+==================+============+===================+================+ | multiple_subcode_blocks | 1 | 100.000 | 481 | 612.10 us | 604.81 us | 605.08 us | 739.61 us | 723.65 us | 294.42 ms | +-------------------------+-----------+-----------+------------+-----------+-----------+------------------+------------+-------------------+----------------+ | single_subcode_blocks | 2 | 236.732 | 449 | 1.58 ms | 1.43 ms | 1.44 ms | 2.98 ms | 2.97 ms | 707.21 ms | +-------------------------+-----------+-----------+------------+-----------+-----------+------------------+------------+-------------------+----------------+ | whole_function | 3 | 337.108 | 482 | 2.08 ms | 2.04 ms | 2.04 ms | 2.24 ms | 2.12 ms | 1.00 s | +-------------------------+-----------+-----------+------------+-----------+-----------+------------------+------------+-------------------+----------------+

Short explanation of result:

• compare %: Depends on the setting for rank_by

  • rank_by='best': takes the function with the fastest best_loop time and set it as 100 % and the other test are compared to that
  • rank_by='average': takes the function with the fastest avg_loop time and set it as 100 % and the other test are compared to that

• loops: are the loops used

• The next five are here to get a feeling of the extremes and how accurate the results might be

  • best_loop: the fastest of all loops
  • second_best_loop: the second fastest of all loops
  • worst_loop: the slowest of all loops
  • second_worst_loop: the second slowest of all loops

  • all_loops time: is the time for all loops combined: because of overhead this is often lower than the benchmarkit__run_sec set

    • also consider that if one times only selected code parts within a function: using START/END TAGS all_loops time might be much lower

      as it reports the measured time and not the total execution time

Uses pythons cProfiler:

RESULT is for each function a separate table which format is conform with reStructuredText

+--------------------------------------------------------------------------------------------------------------------------------+ | ProfileIT name: <example_lambda> profileit__repeat: <2> || total_calls: <8767> primitive_calls: <8767> total_time: <6.12 ms> | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | rank | compare % | func_time | number_of_calls | func_txt | +======+===========+===========+=================+===============================================================================+ | 1 | 36.664 | 2.24 ms | 1,998 | lib/python3.4/random.py:220(_randbelow) | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 2 | 25.740 | 1.57 ms | 2 | lib/python3.4/random.py:258(shuffle) | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 3 | 20.392 | 1.25 ms | 2 | <built-in method sorted> | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 4 | 8.782 | 537.00 us | 2,761 | <method 'getrandbits' of '_random.Random' objects> | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 5 | 4.513 | 276.00 us | 2,000 | Example3ProfileIT.py:60(<lambda>) | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 6 | 2.829 | 173.00 us | 1,998 | <method 'bit_length' of 'int' objects> | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 7 | 1.063 | 65.00 us | 2 | Example3ProfileIT.py:58(example_lambda) | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 8 | 0.016 | 1.00 us | 2 | <built-in method len> | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+ | 9 | 0.000 | 0.00 ns | 2 | <method 'items' of 'dict' objects> | +------+-----------+-----------+-----------------+-------------------------------------------------------------------------------+

Short explanation of result:

• this is a combined result for all runs specified by: profileit__repeat

• compare %: takes the func_time starting with the slowest part and displays

  how many % it took based on the whole execution time (100 %)

A profiler that records the amount of memory for each line This code is based on parts of: https://github.com/fabianp/memory_profiler

Uses pythons dis

for code examples see the files in folder: development-source: Examples

SpeedIT is distributed under the terms of the BSD 3-clause license. Consult LICENSE.rst or http://opensource.org/licenses/BSD-3-Clause.

(c) 2014, peter1000 https://github.com/peter1000 All rights reserved.