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68 lines
2.5 KiB
Text
68 lines
2.5 KiB
Text
stringbench is a set of performance tests comparing byte string
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operations with unicode operations. The two string implementations
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are loosely based on each other and sometimes the algorithm for one is
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faster than the other.
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These test set was started at the Need For Speed sprint in Reykjavik
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to identify which string methods could be sped up quickly and to
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identify obvious places for improvement.
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Here is an example of a benchmark
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@bench('"Andrew".startswith("A")', 'startswith single character', 1000)
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def startswith_single(STR):
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s1 = STR("Andrew")
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s2 = STR("A")
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s1_startswith = s1.startswith
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for x in _RANGE_1000:
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s1_startswith(s2)
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The bench decorator takes three parameters. The first is a short
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description of how the code works. In most cases this is Python code
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snippet. It is not the code which is actually run because the real
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code is hand-optimized to focus on the method being tested.
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The second parameter is a group title. All benchmarks with the same
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group title are listed together. This lets you compare different
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implementations of the same algorithm, such as "t in s"
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vs. "s.find(t)".
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The last is a count. Each benchmark loops over the algorithm either
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100 or 1000 times, depending on the algorithm performance. The output
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time is the time per benchmark call so the reader needs a way to know
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how to scale the performance.
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These parameters become function attributes.
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Here is an example of the output
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========== count newlines
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38.54 41.60 92.7 ...text.with.2000.newlines.count("\n") (*100)
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========== early match, single character
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1.14 1.18 96.8 ("A"*1000).find("A") (*1000)
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0.44 0.41 105.6 "A" in "A"*1000 (*1000)
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1.15 1.17 98.1 ("A"*1000).index("A") (*1000)
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The first column is the run time in milliseconds for byte strings.
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The second is the run time for unicode strings. The third is a
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percentage; byte time / unicode time. It's the percentage by which
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unicode is faster than byte strings.
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The last column contains the code snippet and the repeat count for the
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internal benchmark loop.
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The times are computed with 'timeit.py' which repeats the test more
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and more times until the total time takes over 0.2 seconds, returning
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the best time for a single iteration.
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The final line of the output is the cumulative time for byte and
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unicode strings, and the overall performance of unicode relative to
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bytes. For example
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4079.83 5432.25 75.1 TOTAL
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However, this has no meaning as it evenly weights every test.
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