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See svn.python.org/external/pybench-2.0 for the original import of that version. Note that platform.py was not copied over from pybench-2.0 since it is already part of Python 2.5.
368 lines
14 KiB
Text
368 lines
14 KiB
Text
________________________________________________________________________
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PYBENCH - A Python Benchmark Suite
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________________________________________________________________________
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Extendable suite of of low-level benchmarks for measuring
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the performance of the Python implementation
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(interpreter, compiler or VM).
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pybench is a collection of tests that provides a standardized way to
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measure the performance of Python implementations. It takes a very
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close look at different aspects of Python programs and let's you
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decide which factors are more important to you than others, rather
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than wrapping everything up in one number, like the other performance
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tests do (e.g. pystone which is included in the Python Standard
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Library).
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pybench has been used in the past by several Python developers to
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track down performance bottlenecks or to demonstrate the impact of
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optimizations and new features in Python.
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The command line interface for pybench is the file pybench.py. Run
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this script with option '--help' to get a listing of the possible
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options. Without options, pybench will simply execute the benchmark
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and then print out a report to stdout.
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Micro-Manual
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------------
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Run 'pybench.py -h' to see the help screen. Run 'pybench.py' to run
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the benchmark suite using default settings and 'pybench.py -f <file>'
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to have it store the results in a file too.
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It is usually a good idea to run pybench.py multiple times to see
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whether the environment, timers and benchmark run-times are suitable
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for doing benchmark tests.
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You can use the comparison feature of pybench.py ('pybench.py -c
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<file>') to check how well the system behaves in comparison to a
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reference run.
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If the differences are well below 10% for each test, then you have a
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system that is good for doing benchmark testings. Of you get random
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differences of more than 10% or significant differences between the
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values for minimum and average time, then you likely have some
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background processes running which cause the readings to become
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inconsistent. Examples include: web-browsers, email clients, RSS
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readers, music players, backup programs, etc.
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If you are only interested in a few tests of the whole suite, you can
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use the filtering option, e.g. 'pybench.py -t string' will only
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run/show the tests that have 'string' in their name.
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This is the current output of pybench.py --help:
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"""
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------------------------------------------------------------------------
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PYBENCH - a benchmark test suite for Python interpreters/compilers.
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------------------------------------------------------------------------
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Synopsis:
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pybench.py [option] files...
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Options and default settings:
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-n arg number of rounds (10)
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-f arg save benchmark to file arg ()
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-c arg compare benchmark with the one in file arg ()
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-s arg show benchmark in file arg, then exit ()
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-w arg set warp factor to arg (10)
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-t arg run only tests with names matching arg ()
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-C arg set the number of calibration runs to arg (20)
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-d hide noise in comparisons (0)
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-v verbose output (not recommended) (0)
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--with-gc enable garbage collection (0)
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--with-syscheck use default sys check interval (0)
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--timer arg use given timer (time.time)
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-h show this help text
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--help show this help text
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--debug enable debugging
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--copyright show copyright
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--examples show examples of usage
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Version:
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2.0
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The normal operation is to run the suite and display the
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results. Use -f to save them for later reuse or comparisons.
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Available timers:
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time.time
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time.clock
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systimes.processtime
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Examples:
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python2.1 pybench.py -f p21.pybench
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python2.5 pybench.py -f p25.pybench
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python pybench.py -s p25.pybench -c p21.pybench
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"""
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License
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-------
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See LICENSE file.
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Sample output
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-------------
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"""
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-------------------------------------------------------------------------------
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PYBENCH 2.0
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-------------------------------------------------------------------------------
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* using Python 2.4.2
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* disabled garbage collection
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* system check interval set to maximum: 2147483647
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* using timer: time.time
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Calibrating tests. Please wait...
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Running 10 round(s) of the suite at warp factor 10:
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* Round 1 done in 6.388 seconds.
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* Round 2 done in 6.485 seconds.
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* Round 3 done in 6.786 seconds.
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...
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* Round 10 done in 6.546 seconds.
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-------------------------------------------------------------------------------
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Benchmark: 2006-06-12 12:09:25
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-------------------------------------------------------------------------------
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Rounds: 10
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Warp: 10
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Timer: time.time
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Machine Details:
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Platform ID: Linux-2.6.8-24.19-default-x86_64-with-SuSE-9.2-x86-64
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Processor: x86_64
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Python:
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Executable: /usr/local/bin/python
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Version: 2.4.2
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Compiler: GCC 3.3.4 (pre 3.3.5 20040809)
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Bits: 64bit
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Build: Oct 1 2005 15:24:35 (#1)
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Unicode: UCS2
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Test minimum average operation overhead
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-------------------------------------------------------------------------------
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BuiltinFunctionCalls: 126ms 145ms 0.28us 0.274ms
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BuiltinMethodLookup: 124ms 130ms 0.12us 0.316ms
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CompareFloats: 109ms 110ms 0.09us 0.361ms
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CompareFloatsIntegers: 100ms 104ms 0.12us 0.271ms
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CompareIntegers: 137ms 138ms 0.08us 0.542ms
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CompareInternedStrings: 124ms 127ms 0.08us 1.367ms
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CompareLongs: 100ms 104ms 0.10us 0.316ms
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CompareStrings: 111ms 115ms 0.12us 0.929ms
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CompareUnicode: 108ms 128ms 0.17us 0.693ms
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ConcatStrings: 142ms 155ms 0.31us 0.562ms
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ConcatUnicode: 119ms 127ms 0.42us 0.384ms
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CreateInstances: 123ms 128ms 1.14us 0.367ms
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CreateNewInstances: 121ms 126ms 1.49us 0.335ms
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CreateStringsWithConcat: 130ms 135ms 0.14us 0.916ms
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CreateUnicodeWithConcat: 130ms 135ms 0.34us 0.361ms
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DictCreation: 108ms 109ms 0.27us 0.361ms
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DictWithFloatKeys: 149ms 153ms 0.17us 0.678ms
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DictWithIntegerKeys: 124ms 126ms 0.11us 0.915ms
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DictWithStringKeys: 114ms 117ms 0.10us 0.905ms
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ForLoops: 110ms 111ms 4.46us 0.063ms
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IfThenElse: 118ms 119ms 0.09us 0.685ms
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ListSlicing: 116ms 120ms 8.59us 0.103ms
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NestedForLoops: 125ms 137ms 0.09us 0.019ms
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NormalClassAttribute: 124ms 136ms 0.11us 0.457ms
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NormalInstanceAttribute: 110ms 117ms 0.10us 0.454ms
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PythonFunctionCalls: 107ms 113ms 0.34us 0.271ms
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PythonMethodCalls: 140ms 149ms 0.66us 0.141ms
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Recursion: 156ms 166ms 3.32us 0.452ms
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SecondImport: 112ms 118ms 1.18us 0.180ms
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SecondPackageImport: 118ms 127ms 1.27us 0.180ms
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SecondSubmoduleImport: 140ms 151ms 1.51us 0.180ms
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SimpleComplexArithmetic: 128ms 139ms 0.16us 0.361ms
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SimpleDictManipulation: 134ms 136ms 0.11us 0.452ms
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SimpleFloatArithmetic: 110ms 113ms 0.09us 0.571ms
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SimpleIntFloatArithmetic: 106ms 111ms 0.08us 0.548ms
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SimpleIntegerArithmetic: 106ms 109ms 0.08us 0.544ms
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SimpleListManipulation: 103ms 113ms 0.10us 0.587ms
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SimpleLongArithmetic: 112ms 118ms 0.18us 0.271ms
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SmallLists: 105ms 116ms 0.17us 0.366ms
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SmallTuples: 108ms 128ms 0.24us 0.406ms
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SpecialClassAttribute: 119ms 136ms 0.11us 0.453ms
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SpecialInstanceAttribute: 143ms 155ms 0.13us 0.454ms
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StringMappings: 115ms 121ms 0.48us 0.405ms
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StringPredicates: 120ms 129ms 0.18us 2.064ms
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StringSlicing: 111ms 127ms 0.23us 0.781ms
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TryExcept: 125ms 126ms 0.06us 0.681ms
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TryRaiseExcept: 133ms 137ms 2.14us 0.361ms
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TupleSlicing: 117ms 120ms 0.46us 0.066ms
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UnicodeMappings: 156ms 160ms 4.44us 0.429ms
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UnicodePredicates: 117ms 121ms 0.22us 2.487ms
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UnicodeProperties: 115ms 153ms 0.38us 2.070ms
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UnicodeSlicing: 126ms 129ms 0.26us 0.689ms
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-------------------------------------------------------------------------------
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Totals: 6283ms 6673ms
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"""
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________________________________________________________________________
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Writing New Tests
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________________________________________________________________________
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pybench tests are simple modules defining one or more pybench.Test
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subclasses.
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Writing a test essentially boils down to providing two methods:
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.test() which runs .rounds number of .operations test operations each
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and .calibrate() which does the same except that it doesn't actually
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execute the operations.
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Here's an example:
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------------------
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from pybench import Test
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class IntegerCounting(Test):
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# Version number of the test as float (x.yy); this is important
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# for comparisons of benchmark runs - tests with unequal version
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# number will not get compared.
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version = 1.0
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# The number of abstract operations done in each round of the
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# test. An operation is the basic unit of what you want to
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# measure. The benchmark will output the amount of run-time per
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# operation. Note that in order to raise the measured timings
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# significantly above noise level, it is often required to repeat
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# sets of operations more than once per test round. The measured
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# overhead per test round should be less than 1 second.
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operations = 20
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# Number of rounds to execute per test run. This should be
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# adjusted to a figure that results in a test run-time of between
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# 1-2 seconds (at warp 1).
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rounds = 100000
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def test(self):
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""" Run the test.
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The test needs to run self.rounds executing
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self.operations number of operations each.
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"""
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# Init the test
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a = 1
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# Run test rounds
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#
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# NOTE: Use xrange() for all test loops unless you want to face
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# a 20MB process !
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#
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for i in xrange(self.rounds):
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# Repeat the operations per round to raise the run-time
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# per operation significantly above the noise level of the
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# for-loop overhead.
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# Execute 20 operations (a += 1):
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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a += 1
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def calibrate(self):
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""" Calibrate the test.
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This method should execute everything that is needed to
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setup and run the test - except for the actual operations
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that you intend to measure. pybench uses this method to
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measure the test implementation overhead.
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"""
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# Init the test
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a = 1
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# Run test rounds (without actually doing any operation)
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for i in xrange(self.rounds):
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# Skip the actual execution of the operations, since we
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# only want to measure the test's administration overhead.
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pass
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Registering a new test module
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-----------------------------
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To register a test module with pybench, the classes need to be
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imported into the pybench.Setup module. pybench will then scan all the
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symbols defined in that module for subclasses of pybench.Test and
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automatically add them to the benchmark suite.
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Breaking Comparability
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----------------------
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If a change is made to any individual test that means it is no
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longer strictly comparable with previous runs, the '.version' class
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variable should be updated. Therefafter, comparisons with previous
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versions of the test will list as "n/a" to reflect the change.
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Version History
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---------------
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2.0: rewrote parts of pybench which resulted in more repeatable
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timings:
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- made timer a parameter
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- changed the platform default timer to use high-resolution
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timers rather than process timers (which have a much lower
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resolution)
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- added option to select timer
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- added process time timer (using systimes.py)
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- changed to use min() as timing estimator (average
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is still taken as well to provide an idea of the difference)
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- garbage collection is turned off per default
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- sys check interval is set to the highest possible value
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- calibration is now a separate step and done using
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a different strategy that allows measuring the test
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overhead more accurately
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- modified the tests to each give a run-time of between
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100-200ms using warp 10
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- changed default warp factor to 10 (from 20)
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- compared results with timeit.py and confirmed measurements
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- bumped all test versions to 2.0
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- updated platform.py to the latest version
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- changed the output format a bit to make it look
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nicer
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- refactored the APIs somewhat
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1.3+: Steve Holden added the NewInstances test and the filtering
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option during the NeedForSpeed sprint; this also triggered a long
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discussion on how to improve benchmark timing and finally
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resulted in the release of 2.0
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1.3: initial checkin into the Python SVN repository
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Have fun,
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--
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Marc-Andre Lemburg
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mal@lemburg.com
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