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Much of what you ve heard about code tuning is false. Here are some common misapprehensions:
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Reducing the lines of code in a high-level language improves the speed or size of the resulting machine code false! Many programmers cling tenaciously to the belief that if they can write code in one or two lines, it will be the most efficient possible. Consider the following code that initializes a 10-element array:
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for i = 1 to 10 a[ i ] = i end for
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7 CROSS-REFERENCE Both 8 these code fragments violate
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9 several rules of good pro-
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0 gramming. Readability and
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maintenance are usually more important than execution speed or size, but in this chapter the topic is performance, and that implies a tradeoff with the other objectives. You ll see many examples of coding practices here that aren t recommended in other parts of this book.
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Would you guess that these lines are faster or slower than the following 10 lines that do the same job
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a[ 1 ] = 1 a[ 2 ] = 2 a[ 3 ] = 3 a[ 4 ] = 4 a[ 5 ] = 5 a[ 6 ] = 6 a[ 7 ] = 7 a[ 8 ] = 8 a[ 9 ] = 9 a[ 10 ] = 10
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If you follow the old fewer lines are faster dogma, you ll guess that the first code is faster because it has four fewer lines. Hah! Tests in Visual Basic and Java have shown that the second fragment is at least 60 percent faster than the first. Here are the numbers:
for-Loop Time 8.47 12.6 StraightCode Time 3.16 3.23 Time Savings 63% 74% Performance Ratio 2.5:1 4:1
Language Visual Basic Java
Note: (1) Times in this and the following tables in this chapter are given in seconds and are meaningful only for comparisons across rows in each table. Actual times will vary according to the compiler and compiler options used and the environment in which each test is run. (2) Benchmark results are typically made up of several
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25. Code-Tuning Strategies
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thousand to many million executions of the code fragments to smooth out sample-tosample fluctuations in the results. (3) Specific brands and versions of compilers aren t indicated. Performance characteristics vary significantly from brand to brand and from version to version. (4) Comparisons among results from different languages aren t always meaningful because compilers for different languages don t always offer comparable code-generation options. (5) The results shown for interpreted languages (PHP and Python) are typically based on less than 1% of the test runs used for the other languages. (6) Some of the time savings percentages might not be exactly reproducible from the data in these tables due to rounding of the straight time and code-tuned time entries.
This certainly doesn t imply the conclusion that increasing the number of lines of high-level language code always improves speed or reduces size. It does imply that regardless of the aesthetic appeal of writing something with the fewest lines of code, there s no predictable relationship between the number of lines of code in a high-level language and a program s ultimate size and speed.
Certain operations are probably faster or smaller than others false! There s no room for probably when you re talking about performance. You must always measure performance to know whether your changes helped or hurt your program. The rules of the game change every time you change languages, compilers, versions of compilers, libraries, versions of libraries, processor, amount of memory on the machine, color of shirt you re wearing and so. (These are all serious except the last one.) What was true on one machine with one set of tools can easily be false on another machine with a different set of tools.
This phenomenon suggests several reasons not to improve performance by code tuning. If you want your program to be portable, techniques that improve performance in one environment can degrade it in others. If you change compilers or upgrade, the new compiler might automatically optimize code the way you were hand-tuning it, and your work will have been wasted. Even worse, your code tuning might defeat more powerful compiler optimizations that have been designed to work with straightforward code. When you tune code, you re implicitly signing up to reprofile each optimization every time you change your compiler brand, compiler version, library version, and so. If you don t reprofile, an optimization that improves performance under one version of a compiler or library might well degrade performance when you change the build environment.
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