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    <p>I think there are a few reasons for this. One is that in many cases, simply getting the job done is more important than making it perform as well as possible. A lot of software that I write is stuff that will only be run on occasion on small data sets, or stuff where the performance implications are pretty trivial (it's a loop that does a fixed computation on each element, so it's trivially O(n)). For most of this software, it would be silly to spend time analyzing the running time in detail.</p> <p>Another reason is that software is very easy to change later on. Once you've built a bridge, any fixes can be incredibly expensive, so it's good to be very sure of your design before you do it. In software, unless you've made a horrible architectural choice early on, you can generally find and optimize performance hot spots once you have some more real-world data about how it performs. In order to avoid those horrible architectural choices, you can generally do approximate, back-of-the-envelope calculations (make sure you're not using an O(2^n) algorithm on a large data set, and estimate within a factor of 10 or so how many resources you'll need for the heaviest load you expect). These do require some analysis, but usually it can be pretty quick and off the cuff.</p> <p>And then there are cases in which you really, really do need to squeeze the ultimate performance out of a system. In these case, people frequently do actually sit down, work out the performance characteristics of the systems they are working with, and do very detailed analyses. See, for instance, Ulrich Drepper's very impressive paper <a href="http://people.redhat.com/drepper/cpumemory.pdf" rel="noreferrer">What Every Programmer Should Know About Memory (pdf)</a>.</p>
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