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25. Code-Tuning Strategies
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Even though most individual optimizations were small, cumulatively they were significant. To judge from the percentage improvements, no three or even four optimizations would have met my performance goal. But the final combination was effective. The moral of the story is that if you dig deep enough, you can make some surprising gains. The code tuning I did in this case is the most aggressive code tuning I ve ever done. At the same time, the final code is the most unreadable, unmaintainable code I ve ever written. The initial algorithm is complicated. The code resulting from the high-level language transformation was barely readable. The translation to assembler produced a single 500-line routine that I m afraid to look at. In general, this relationship between code tuning and code quality holds true. Here s a table that shows a history of the optimizations:
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The techniques listed in this table are described in 26, Code-Tuning Techniques.
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Optimization Implement initially straightforward Convert from bit fields to arrays Unroll innermost for loop Remove final permutation Combine two variables Use a logical identity to combine the first two steps of the DES algorithm Make two variables share the same memory to reduce data shuttling in inner loop Make two variables share the same memory to reduce data shuttling in outer loop Unfold all loops and use literal array subscripts Remove routine calls and put all the code in line Rewrite the whole routine in assembler Final
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Benchmark Time 21:40 7:30 6:00 5:24 5:06 4:30
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Improvement 65% 20% 10% 5% 12%
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3:09
1:36 0:45 0:22 0:22
49% 53% 51% 98%
Note: The steady progress of optimizations in this table doesn t imply that all optimizations work. I haven t shown all the things I tried that doubled the run time. At least two-thirds of the optimizations I tried didn t work.
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25. Code-Tuning Strategies
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25.6 Summary of the Approach to Code Tuning
Here are the steps you should take as you consider whether code tuning can help you improve the performance of a program: 1. Develop the software using well-designed code that s easy to understand and modify. 2. If performance is poor, a. Save a working version of the code so that you can get back to the last known good state.
b. Measure the system to find hot spots. c. Determine whether the weak performance comes from inadequate design, data types, or algorithms and whether code tuning is appropriate. If code tuning isn t appropriate, go back to step 1.
d. Tune the bottleneck identified in step (c). e. f. Measure each improvement one at a time. If an improvement doesn t improve the code, revert to the code saved in step (a). (Typically, more than half the attempted tunings will produce only a negligible improvement in performance or degrade performance.)
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3. Repeat from step 2.
Additional Resources
Performance
Smith, Connie U. and Lloyd G. Williams. Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software, Boston, Mass.: Addison Wesley, 2002. This book covers software performance engineering, an approach for building performance into software systems at all stages of development. It makes extensive use of examples and case studies for several kinds of programs. It includes specific recommendations for web applications and pays special attention to scalability.
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25. Code-Tuning Strategies
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Newcomer, Joseph M. Optimization: Your Worst Enemy, May 2000, www.flounder.com/optimization.htm. Newcomer is an experienced systems programmer who describes the various pitfalls of ineffective optimization strategies in graphic detail.
Algorithms and Data Types
Knuth, Donald. The Art of Computer Programming, vol. 1, Fundamental Algorithms, 3d ed. Reading, Mass.: Addison-Wesley, 1997. Knuth, Donald. The Art of Computer Programming, vol. 2, Seminumerical Algorithms, 3d ed. Reading, Mass.: Addison-Wesley, 1997. Knuth, Donald. The Art of Computer Programming, vol. 3, Sorting and Searching, 2d ed. Reading, Mass.: Addison-Wesley, 1998. These are the first three volumes of a series that was originally intended to grow to seven volumes. They can be somewhat intimidating. In addition to the English description of the algorithms, they re described in mathematical notation or MIX, an assembly language for the imaginary MIX computer. The books contain exhaustive details on a huge number of topics, and if you have an intense interest in a particular algorithm, you won t find a better reference. Sedgewick, Robert. Algorithms in Java, Parts 1-4, 3d ed. Boston, Mass.: Addison-Wesley, 2002. This book s four parts contain a survey of the best methods of solving a wide variety of problems. Its subject areas include fundamentals, sorting, searching, abstract data type implementation, and advanced topics. Sedgewick s Algorithms in Java, Part 5, 3d ed. (2003) covers graph algorithms. Sedgewick s Algorithms in C++, Parts 1-4, 3d ed. (1998), Algorithms in C++, Part 5, 3d ed. (2002), Algorithms in C, Parts 1-4, 3d ed. (1997), and Algorithms in C, Part 5, 3d ed. (2001) are similarly organized. Sedgewick was a Ph.D. student of Knuth s.
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