________________________________________________________________________

PYBENCH - A Python Benchmark Suite

________________________________________________________________________

     Extendable suite of of low-level benchmarks for measuring

          the performance of the Python implementation 

                 (interpreter, compiler or VM).

pybench is a collection of tests that provides a standardized way to

measure the performance of Python implementations. It takes a very

close look at different aspects of Python programs and let's you

decide which factors are more important to you than others, rather

than wrapping everything up in one number, like the other performance

tests do (e.g. pystone which is included in the Python Standard

Library).

pybench has been used in the past by several Python developers to

track down performance bottlenecks or to demonstrate the impact of

optimizations and new features in Python.

The command line interface for pybench is the file pybench.py. Run

this script with option '--help' to get a listing of the possible

options. Without options, pybench will simply execute the benchmark

and then print out a report to stdout.

Micro-Manual

------------

Run 'pybench.py -h' to see the help screen.  Run 'pybench.py' to run

the benchmark suite using default settings and 'pybench.py -f <file>'

to have it store the results in a file too.

It is usually a good idea to run pybench.py multiple times to see

whether the environment, timers and benchmark run-times are suitable

for doing benchmark tests. 

You can use the comparison feature of pybench.py ('pybench.py -c

<file>') to check how well the system behaves in comparison to a

reference run. 

If the differences are well below 10% for each test, then you have a

system that is good for doing benchmark testings.  Of you get random

differences of more than 10% or significant differences between the

values for minimum and average time, then you likely have some

background processes running which cause the readings to become

inconsistent. Examples include: web-browsers, email clients, RSS

readers, music players, backup programs, etc.

If you are only interested in a few tests of the whole suite, you can

use the filtering option, e.g. 'pybench.py -t string' will only

run/show the tests that have 'string' in their name.

This is the current output of pybench.py --help:

"""

------------------------------------------------------------------------

PYBENCH - a benchmark test suite for Python interpreters/compilers.

------------------------------------------------------------------------

Synopsis:

 pybench.py [option] files...

Options and default settings:

  -n arg           number of rounds (10)

  -f arg           save benchmark to file arg ()

  -c arg           compare benchmark with the one in file arg ()

  -s arg           show benchmark in file arg, then exit ()

  -w arg           set warp factor to arg (10)

  -t arg           run only tests with names matching arg ()

  -C arg           set the number of calibration runs to arg (20)

  -d               hide noise in comparisons (0)

  -v               verbose output (not recommended) (0)

  --with-gc        enable garbage collection (0)

  --with-syscheck  use default sys check interval (0)

  --timer arg      use given timer (time.time)

  -h               show this help text

  --help           show this help text

  --debug          enable debugging

  --copyright      show copyright

  --examples       show examples of usage

Version:

 2.0

The normal operation is to run the suite and display the

results. Use -f to save them for later reuse or comparisons.

Available timers:

   time.time

   time.clock

   systimes.processtime

Examples:

python2.1 pybench.py -f p21.pybench

python2.5 pybench.py -f p25.pybench

python pybench.py -s p25.pybench -c p21.pybench

"""

License

-------

See LICENSE file.

Sample output

-------------

"""

-------------------------------------------------------------------------------

PYBENCH 2.0

-------------------------------------------------------------------------------

* using Python 2.4.2

* disabled garbage collection

* system check interval set to maximum: 2147483647

* using timer: time.time

Calibrating tests. Please wait...

Running 10 round(s) of the suite at warp factor 10:

* Round 1 done in 6.388 seconds.

* Round 2 done in 6.485 seconds.

* Round 3 done in 6.786 seconds.

...

* Round 10 done in 6.546 seconds.

-------------------------------------------------------------------------------

Benchmark: 2006-06-12 12:09:25

-------------------------------------------------------------------------------

    Rounds: 10

    Warp:   10

    Timer:  time.time

    Machine Details:

       Platform ID:  Linux-2.6.8-24.19-default-x86_64-with-SuSE-9.2-x86-64

       Processor:    x86_64

    Python:

       Executable:   /usr/local/bin/python

       Version:      2.4.2

       Compiler:     GCC 3.3.4 (pre 3.3.5 20040809)

       Bits:         64bit

       Build:        Oct  1 2005 15:24:35 (#1)

       Unicode:      UCS2

Test                             minimum  average  operation  overhead

-------------------------------------------------------------------------------

          BuiltinFunctionCalls:    126ms    145ms    0.28us    0.274ms

           BuiltinMethodLookup:    124ms    130ms    0.12us    0.316ms

                 CompareFloats:    109ms    110ms    0.09us    0.361ms

         CompareFloatsIntegers:    100ms    104ms    0.12us    0.271ms

               CompareIntegers:    137ms    138ms    0.08us    0.542ms

        CompareInternedStrings:    124ms    127ms    0.08us    1.367ms

                  CompareLongs:    100ms    104ms    0.10us    0.316ms

                CompareStrings:    111ms    115ms    0.12us    0.929ms

                CompareUnicode:    108ms    128ms    0.17us    0.693ms

                 ConcatStrings:    142ms    155ms    0.31us    0.562ms

                 ConcatUnicode:    119ms    127ms    0.42us    0.384ms

               CreateInstances:    123ms    128ms    1.14us    0.367ms

            CreateNewInstances:    121ms    126ms    1.49us    0.335ms

       CreateStringsWithConcat:    130ms    135ms    0.14us    0.916ms

       CreateUnicodeWithConcat:    130ms    135ms    0.34us    0.361ms

                  DictCreation:    108ms    109ms    0.27us    0.361ms

             DictWithFloatKeys:    149ms    153ms    0.17us    0.678ms

           DictWithIntegerKeys:    124ms    126ms    0.11us    0.915ms

            DictWithStringKeys:    114ms    117ms    0.10us    0.905ms

                      ForLoops:    110ms    111ms    4.46us    0.063ms

                    IfThenElse:    118ms    119ms    0.09us    0.685ms

                   ListSlicing:    116ms    120ms    8.59us    0.103ms

                NestedForLoops:    125ms    137ms    0.09us    0.019ms

          NormalClassAttribute:    124ms    136ms    0.11us    0.457ms

       NormalInstanceAttribute:    110ms    117ms    0.10us    0.454ms

           PythonFunctionCalls:    107ms    113ms    0.34us    0.271ms

             PythonMethodCalls:    140ms    149ms    0.66us    0.141ms

                     Recursion:    156ms    166ms    3.32us    0.452ms

                  SecondImport:    112ms    118ms    1.18us    0.180ms

           SecondPackageImport:    118ms    127ms    1.27us    0.180ms

         SecondSubmoduleImport:    140ms    151ms    1.51us    0.180ms

       SimpleComplexArithmetic:    128ms    139ms    0.16us    0.361ms

        SimpleDictManipulation:    134ms    136ms    0.11us    0.452ms

         SimpleFloatArithmetic:    110ms    113ms    0.09us    0.571ms

      SimpleIntFloatArithmetic:    106ms    111ms    0.08us    0.548ms

       SimpleIntegerArithmetic:    106ms    109ms    0.08us    0.544ms

        SimpleListManipulation:    103ms    113ms    0.10us    0.587ms

          SimpleLongArithmetic:    112ms    118ms    0.18us    0.271ms

                    SmallLists:    105ms    116ms    0.17us    0.366ms

                   SmallTuples:    108ms    128ms    0.24us    0.406ms

         SpecialClassAttribute:    119ms    136ms    0.11us    0.453ms

      SpecialInstanceAttribute:    143ms    155ms    0.13us    0.454ms

                StringMappings:    115ms    121ms    0.48us    0.405ms

              StringPredicates:    120ms    129ms    0.18us    2.064ms

                 StringSlicing:    111ms    127ms    0.23us    0.781ms

                     TryExcept:    125ms    126ms    0.06us    0.681ms

                TryRaiseExcept:    133ms    137ms    2.14us    0.361ms

                  TupleSlicing:    117ms    120ms    0.46us    0.066ms

               UnicodeMappings:    156ms    160ms    4.44us    0.429ms

             UnicodePredicates:    117ms    121ms    0.22us    2.487ms

             UnicodeProperties:    115ms    153ms    0.38us    2.070ms

                UnicodeSlicing:    126ms    129ms    0.26us    0.689ms

-------------------------------------------------------------------------------

Totals:                           6283ms   6673ms

"""

________________________________________________________________________

Writing New Tests

________________________________________________________________________

pybench tests are simple modules defining one or more pybench.Test

subclasses.

Writing a test essentially boils down to providing two methods:

.test() which runs .rounds number of .operations test operations each

and .calibrate() which does the same except that it doesn't actually

execute the operations.

Here's an example:

------------------

from pybench import Test

class IntegerCounting(Test):

    # Version number of the test as float (x.yy); this is important

    # for comparisons of benchmark runs - tests with unequal version

    # number will not get compared.

    version = 1.0

    # The number of abstract operations done in each round of the

    # test. An operation is the basic unit of what you want to

    # measure. The benchmark will output the amount of run-time per

    # operation. Note that in order to raise the measured timings

    # significantly above noise level, it is often required to repeat

    # sets of operations more than once per test round. The measured

    # overhead per test round should be less than 1 second.

    operations = 20

    # Number of rounds to execute per test run. This should be

    # adjusted to a figure that results in a test run-time of between

    # 1-2 seconds (at warp 1).

    rounds = 100000

    def test(self):

	""" Run the test.

	    The test needs to run self.rounds executing

	    self.operations number of operations each.

        """

        # Init the test

        a = 1

        # Run test rounds

	#

        # NOTE: Use xrange() for all test loops unless you want to face

	# a 20MB process !

	#

        for i in xrange(self.rounds):

            # Repeat the operations per round to raise the run-time

            # per operation significantly above the noise level of the

            # for-loop overhead. 

	    # Execute 20 operations (a += 1):

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

            a += 1

    def calibrate(self):

	""" Calibrate the test.

	    This method should execute everything that is needed to

	    setup and run the test - except for the actual operations

	    that you intend to measure. pybench uses this method to

            measure the test implementation overhead.

        """

        # Init the test

        a = 1

        # Run test rounds (without actually doing any operation)

        for i in xrange(self.rounds):

	    # Skip the actual execution of the operations, since we

	    # only want to measure the test's administration overhead.

            pass

Registering a new test module

-----------------------------

To register a test module with pybench, the classes need to be

imported into the pybench.Setup module. pybench will then scan all the

symbols defined in that module for subclasses of pybench.Test and

automatically add them to the benchmark suite.

Breaking Comparability

----------------------

If a change is made to any individual test that means it is no

longer strictly comparable with previous runs, the '.version' class

variable should be updated. Therefafter, comparisons with previous

versions of the test will list as "n/a" to reflect the change.

Version History

---------------

  2.0: rewrote parts of pybench which resulted in more repeatable

       timings:

        - made timer a parameter

        - changed the platform default timer to use high-resolution

          timers rather than process timers (which have a much lower

          resolution)

        - added option to select timer

        - added process time timer (using systimes.py)

        - changed to use min() as timing estimator (average

          is still taken as well to provide an idea of the difference)

        - garbage collection is turned off per default

        - sys check interval is set to the highest possible value

        - calibration is now a separate step and done using

          a different strategy that allows measuring the test

          overhead more accurately

        - modified the tests to each give a run-time of between

          100-200ms using warp 10

        - changed default warp factor to 10 (from 20)

        - compared results with timeit.py and confirmed measurements

        - bumped all test versions to 2.0

        - updated platform.py to the latest version

        - changed the output format a bit to make it look

          nicer

        - refactored the APIs somewhat

  1.3+: Steve Holden added the NewInstances test and the filtering 

       option during the NeedForSpeed sprint; this also triggered a long 

       discussion on how to improve benchmark timing and finally

       resulted in the release of 2.0

  1.3: initial checkin into the Python SVN repository

Have fun,

--

Marc-Andre Lemburg

mal@lemburg.com