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A substantial portion of a knowledge worker's life may be spent waiting for a computer program to produce output. Users and organizations control their wait time by purchasing faster computers, adding memory, or using faster network connections.
Developers of application programs have a responsibility to design their programs make the best use of these limited and expensive resources. This document describes techniques for optimizing improving the speed of computer programs written in C. It focuses on minimizing time spent by the CPU and gives sample source code transformations that often yield improvements.
It's often difficult to guess what part of your program is consuming the most resources, and if you base your optimization efforts on speculation instead of real data, you'll waste a lot of time speeding up the parts of your program that were fast already. Once you have identified a bottleneck, for example a loop that is executed thousands of times, remember that the best thing to do is to redesign the program so that it doesn't need to execute the loop thousands of times.
Optimization is simply waste of programmer time if any of these statements are true: If it's a report-generator program which only needs to be run once a day, the user may start it before going off to lunch and if so there's really no point in making it finish before they get back.
If it's being invoked from within another program that's even slower than yours, again the user will notice no difference. But if it handles mouse-tracking events for a GUI the user will complain about any noticeable delay. Given that optimizing is reasonable, compile in full optimize mode and run your program on "real-world" input data. If you don't have access to real input data, choose your test input data with care: You may have more "time" commands on your system than you realize: You can also get performance information from getrusage if you have it, and of course from profiling programs like gprof, prof, and tcov.
Run your program, again on real world data, and generate a profiling report. Figure out which function uses the most CPU time, then look it over very carefully and see if any of these approaches might be useful. Make one change at a time, then run the profiler again, repeating the process until there is no obvious bottleneck or the program runs sufficiently fast.
The first four are quite important; the rest are in no particular order. Become familiar with the body of literature that describes your specialty and learn and use the most appropriate algorithms, even if you didn't come up with them yourself. You should be familiar with O n notation, which is defined in many computer science texts. Some of the obvious replacements: