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payment = loanAmount / ( ( 1.0 Math.pow( 1.0 + ( interestRate / 12.0 ), -months ) ) / ( interestRate / 12.0 ) );
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In this sample, you can assign interestRate/12.0 to a variable that is then referenced twice rather than computing the expression twice. If you have chosen the variable name well, this optimization can improve the code s readability at the same time that it improves performance. The next example shows the revised code.
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Java Example of Eliminating a Common Subexpression
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monthlyInterest = interestRate / 12.0; payment = loanAmount / ( ( 1.0 Math.pow( 1.0 + monthlyInterest, -months ) ) / monthlyInterest );
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The savings in this case don t seem impressive:
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Language Java Python Straight Time 2.94 3.91 Code-Tuned Time 2.83 3.94 Time Savings 4% -1%
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de Complete
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26. Code-Tuning Techniques
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It appears that the Math.pow() routine is so costly that it overshadows the savings from subexpression elimination. Or possibly the subexpression is already being eliminated by the compiler. If the subexpression were a bigger part of the cost of the whole expression or if the compiler optimizer were less effective, the optimization might have more impact.
26.5 Routines
0 CROSS-REFERENCE
1 details on working with
routines, see 7, High-Quality Routines.
One of the most powerful tools in code tuning is a good routine decomposition. Small, well-defined routines save space because they take the place of doing jobs separately in multiple places. They make a program easy to optimize because you can refactor code in one routine and thus improve every routine that calls it. Small routines are relatively easy to rewrite in assembler. Long, tortuous routines are hard enough to understand on their own; in assembler they re impossible.
Rewrite Routines In Line
In the early days of computer programming, some machines imposed prohibitive performance penalties for calling a routine. A call to a routine meant that the operating system had to swap out the program, swap in a directory of routines, swap in the particular routine, execute the routine, swap out the routine, and swap the calling routine back in. All this swapping chewed up resources and made the program slow. Modern computers collect a far smaller toll for calling a routine. Here are the results of putting a string-copy routine in line:
Inline-Code Time 0.431 14.4
Language C++ Java
Routine Time 0.471 13.1
Time Savings 8% -10%
In some cases, you might be able to save a few nanoseconds by putting the code from a routine into the program directly where it s needed using a language feature like C++ s inline keyword. If you re working in a language that doesn t support inline directly but that does have a macro preprocessor, you can use a macro to put the code in, switching it in and out as needed. But modern machines and modern means any machine you re ever likely to work on impose virtually no penalty for calling a routine. As the example shows, you re as likely to degrade performance by keeping code inline as to optimize it.
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26. Code-Tuning Techniques
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26.6 Recoding in Assembler
One longstanding piece of conventional wisdom that shouldn t be left unmentioned is the advice that when you run into a performance bottleneck, you should recode in assembler. Recoding in assembler tends to improve both speed and code size. Here is a typical approach to optimizing with assembler: 1. Write 100 percent of an application in a high-level language. 2. Fully test the application, and verify that it s correct.
For details on the phenomenon of a small percentage of a program accounting for most of its run time, see The Pareto Principle in Section 25.2.
1 CROSS-REFERENCE
3. If performance improvements are needed after that, profile the application to identify hot spots. Since about 5 percent of a program usually accounts for about 50 percent of the running time, you can usually identify small pieces of the program as hot spots. 4. Recode a few small pieces in assembler to improve overall performance. Whether you follow this well-beaten path depends on how comfortable you are with assembler, how well-suited the problem is to assembler, and on your level of desperation. I got my first exposure to assembler on the DES encryption program I mentioned in the previous chapter. I had tried every optimization I d ever heard of, and the program was still twice as slow as the speed goal. Recoding part of the program in assembler was the only remaining option. As an assembler novice, about all I could do was make a straight translation from a high-level language to assembler, but I got a 50 percent improvement even at that rudimentary level. Suppose you have a routine that converts binary data to uppercase ASCII characters. The next example shows the Delphi code to do it.
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