mirror of https://github.com/python/cpython
373 lines
14 KiB
Plaintext
373 lines
14 KiB
Plaintext
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________________________________________________________________________
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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.
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Run 'pybench.py' to just let the benchmark suite do it's thing and
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'pybench.py -f <file>' to have it store the results in a file too.
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This is the current output of pybench.py --help:
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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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-S show statistics of benchmarks (0)
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-w arg set warp factor to arg (20)
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-d hide noise in compares (0)
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--no-gc disable garbage collection (0)
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-v generate verbose output
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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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1.3
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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 comparisms.
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Examples:
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python1.5 pybench.py -w 100 -f p15
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python1.4 pybench.py -w 100 -f p14
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python pybench.py -s p15 -c p14
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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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PYBENCH 1.3
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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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Executable: /home/lemburg/projects/Python/Installation/bin/python
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Python: 2.5a1.0
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Compiler: GCC 3.3.4 (pre 3.3.5 20040809)
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Build: Apr 9 2006 01:50:57 (#trunk)
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Searching for tests...
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BuiltinFunctionCalls
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BuiltinMethodLookup
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CompareFloats
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CompareFloatsIntegers
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CompareIntegers
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CompareInternedStrings
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CompareLongs
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CompareStrings
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CompareUnicode
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ConcatStrings
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ConcatUnicode
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CreateInstances
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CreateStringsWithConcat
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CreateUnicodeWithConcat
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DictCreation
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DictWithFloatKeys
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DictWithIntegerKeys
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DictWithStringKeys
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ForLoops
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IfThenElse
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ListSlicing
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NestedForLoops
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NormalClassAttribute
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NormalInstanceAttribute
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PythonFunctionCalls
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PythonMethodCalls
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Recursion
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SecondImport
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SecondPackageImport
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SecondSubmoduleImport
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SimpleComplexArithmetic
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SimpleDictManipulation
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SimpleFloatArithmetic
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SimpleIntFloatArithmetic
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SimpleIntegerArithmetic
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SimpleListManipulation
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SimpleLongArithmetic
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SmallLists
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SmallTuples
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SpecialClassAttribute
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SpecialInstanceAttribute
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StringMappings
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StringPredicates
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StringSlicing
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TryExcept
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TryRaiseExcept
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TupleSlicing
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UnicodeMappings
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UnicodePredicates
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UnicodeProperties
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UnicodeSlicing
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Running 10 round(s) of the suite:
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...
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Round 10 real abs overhead
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BuiltinFunctionCalls: 0.030r 0.030a 0.000o
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BuiltinMethodLookup: 0.059r 0.060a 0.001o
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CompareFloats: 0.050r 0.050a 0.000o
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CompareFloatsIntegers: 0.050r 0.050a 0.000o
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CompareIntegers: 0.070r 0.070a 0.000o
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CompareInternedStrings: 0.039r 0.040a 0.001o
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CompareLongs: 0.050r 0.050a 0.000o
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CompareStrings: 0.060r 0.060a 0.000o
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CompareUnicode: 0.060r 0.060a 0.000o
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ConcatStrings: 0.040r 0.040a 0.000o
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ConcatUnicode: 0.050r 0.050a 0.000o
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CreateInstances: 0.050r 0.050a 0.000o
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CreateStringsWithConcat: 0.029r 0.030a 0.001o
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CreateUnicodeWithConcat: 0.060r 0.060a 0.000o
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DictCreation: 0.040r 0.040a 0.000o
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DictWithFloatKeys: 0.089r 0.090a 0.000o
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DictWithIntegerKeys: 0.059r 0.060a 0.001o
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DictWithStringKeys: 0.070r 0.070a 0.001o
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ForLoops: 0.050r 0.050a 0.000o
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IfThenElse: 0.070r 0.070a 0.000o
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ListSlicing: 0.030r 0.030a 0.000o
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NestedForLoops: 0.030r 0.030a 0.000o
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NormalClassAttribute: 0.060r 0.060a 0.000o
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NormalInstanceAttribute: 0.060r 0.060a 0.000o
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PythonFunctionCalls: 0.060r 0.060a 0.000o
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PythonMethodCalls: 0.050r 0.050a 0.000o
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Recursion: 0.050r 0.050a 0.000o
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SecondImport: 0.030r 0.030a 0.000o
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SecondPackageImport: 0.030r 0.030a 0.000o
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SecondSubmoduleImport: 0.040r 0.040a 0.000o
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SimpleComplexArithmetic: 0.030r 0.030a 0.000o
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SimpleDictManipulation: 0.040r 0.040a 0.000o
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SimpleFloatArithmetic: 0.050r 0.050a 0.001o
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SimpleIntFloatArithmetic: 0.060r 0.060a 0.000o
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SimpleIntegerArithmetic: 0.060r 0.060a 0.000o
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SimpleListManipulation: 0.030r 0.030a 0.000o
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SimpleLongArithmetic: 0.030r 0.030a 0.000o
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SmallLists: 0.050r 0.050a 0.000o
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SmallTuples: 0.050r 0.050a 0.000o
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SpecialClassAttribute: 0.060r 0.060a 0.000o
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SpecialInstanceAttribute: 0.079r 0.080a 0.001o
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StringMappings: 0.060r 0.060a 0.000o
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StringPredicates: 0.049r 0.050a 0.001o
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StringSlicing: 0.039r 0.040a 0.000o
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TryExcept: 0.079r 0.080a 0.001o
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TryRaiseExcept: 0.059r 0.060a 0.001o
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TupleSlicing: 0.050r 0.050a 0.000o
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UnicodeMappings: 0.070r 0.070a 0.001o
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UnicodePredicates: 0.059r 0.060a 0.001o
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UnicodeProperties: 0.059r 0.060a 0.001o
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UnicodeSlicing: 0.050r 0.050a 0.000o
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----------------------
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Average round time: 2.937 seconds
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Tests: per run per oper. overhead
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------------------------------------------------------------------------
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BuiltinFunctionCalls: 29.85 ms 0.23 us 0.00 ms
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BuiltinMethodLookup: 66.85 ms 0.13 us 0.50 ms
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CompareFloats: 43.00 ms 0.10 us 0.00 ms
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CompareFloatsIntegers: 51.80 ms 0.12 us 0.00 ms
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CompareIntegers: 70.70 ms 0.08 us 0.50 ms
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CompareInternedStrings: 41.40 ms 0.08 us 0.50 ms
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CompareLongs: 47.90 ms 0.11 us 0.00 ms
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CompareStrings: 58.50 ms 0.12 us 0.50 ms
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CompareUnicode: 56.55 ms 0.15 us 0.50 ms
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ConcatStrings: 44.75 ms 0.30 us 0.00 ms
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ConcatUnicode: 54.55 ms 0.36 us 0.50 ms
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CreateInstances: 50.95 ms 1.21 us 0.00 ms
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CreateStringsWithConcat: 28.85 ms 0.14 us 0.50 ms
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CreateUnicodeWithConcat: 53.75 ms 0.27 us 0.00 ms
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DictCreation: 41.90 ms 0.28 us 0.00 ms
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DictWithFloatKeys: 88.50 ms 0.15 us 0.50 ms
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DictWithIntegerKeys: 62.55 ms 0.10 us 0.50 ms
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DictWithStringKeys: 60.50 ms 0.10 us 0.50 ms
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ForLoops: 46.90 ms 4.69 us 0.00 ms
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IfThenElse: 60.55 ms 0.09 us 0.00 ms
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ListSlicing: 29.90 ms 8.54 us 0.00 ms
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NestedForLoops: 33.95 ms 0.10 us 0.00 ms
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NormalClassAttribute: 62.75 ms 0.10 us 0.50 ms
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NormalInstanceAttribute: 61.80 ms 0.10 us 0.50 ms
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PythonFunctionCalls: 60.00 ms 0.36 us 0.00 ms
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PythonMethodCalls: 50.00 ms 0.67 us 0.00 ms
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Recursion: 46.85 ms 3.75 us 0.00 ms
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SecondImport: 35.00 ms 1.40 us 0.00 ms
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SecondPackageImport: 32.00 ms 1.28 us 0.00 ms
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SecondSubmoduleImport: 38.00 ms 1.52 us 0.00 ms
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SimpleComplexArithmetic: 26.85 ms 0.12 us 0.00 ms
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SimpleDictManipulation: 40.85 ms 0.14 us 0.00 ms
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SimpleFloatArithmetic: 48.70 ms 0.09 us 0.50 ms
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SimpleIntFloatArithmetic: 57.70 ms 0.09 us 0.00 ms
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SimpleIntegerArithmetic: 58.75 ms 0.09 us 0.50 ms
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SimpleListManipulation: 34.80 ms 0.13 us 0.00 ms
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SimpleLongArithmetic: 30.95 ms 0.19 us 0.50 ms
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SmallLists: 47.60 ms 0.19 us 0.00 ms
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SmallTuples: 48.80 ms 0.20 us 0.50 ms
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SpecialClassAttribute: 61.70 ms 0.10 us 0.00 ms
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SpecialInstanceAttribute: 76.70 ms 0.13 us 0.50 ms
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StringMappings: 58.70 ms 0.47 us 0.00 ms
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StringPredicates: 50.00 ms 0.18 us 1.00 ms
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StringSlicing: 39.65 ms 0.23 us 0.50 ms
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TryExcept: 84.45 ms 0.06 us 0.50 ms
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TryRaiseExcept: 61.75 ms 4.12 us 0.50 ms
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TupleSlicing: 48.95 ms 0.47 us 0.00 ms
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UnicodeMappings: 71.50 ms 3.97 us 0.50 ms
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UnicodePredicates: 52.75 ms 0.23 us 1.00 ms
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UnicodeProperties: 61.90 ms 0.31 us 1.00 ms
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UnicodeSlicing: 53.75 ms 0.31 us 0.50 ms
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------------------------------------------------------------------------
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Average round time: 2937.00 ms
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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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# 20-50 seconds.
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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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Have fun,
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--
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Marc-Andre Lemburg
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mal@lemburg.com
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