How does an organization which has been built on 99% not doing optimization recognize the one percent where it matters a lot?
And when you do identify the 1%, you need to be testing optimizations with a profiler constantly while optimizing. Profile. Do some optimization. Profile again. Roll back if not successful. Repeat until done. It's impossible to optimize well if you're not doing profiling.
The ultimate tools would be either Intel's profiling tools, or ARM's profiling suite (both very expensive). But MSVC and GCC do a fantastic job of scheduling instructions to avoid pipeline stalls these days, so these deep profiling tools are unlikely to gain more than 2 or 3% performance increases these days. (Worthwhile pretty much only if you're writing GPU drivers for NVidia or AMD).
Taken as a given that 100% of your code is at least algorithmically correct in the first place. (Appropriately better than O(N^2) whenever possible).
- Former writer of graphics drivers, currently audio DSP engineer.
VTune allows you to determine where pipeline stalls are occurring at the instruction level (for that last 2 or 3% gain in performance). I haven't worked with ARM profilers (way out of my price range), but I assume, given the exorbitant price, they provide the same sort of in-depth analysis. Probably a handful of people on the planet that need that kind of in-depth analysis.
If you can't recognize the percent where it matters a lot, clearly that percent does not exist and you're bothering about nothing.
I see a lot of talk about the importance of tuning Formula 1 cars in a world where everyone drives ford fiestas.
If you're lucky enough to eat your own dogfood, run unit tests against a copy(!) of your in-house database. A utility to anonymize the data was a fairly significant investment, but absolutely worth it in the long run. Being able to run and monitor benchmark unit tests for selected critical operations on enterprise-scale test data as part of the continuous-build process: fabulous!
How this typically works with performance is very similar, with a small team working to identify problematic areas where optimization would drive the highest impact, and the rest of the organization keeping performance in mind but not otherwise concerned with it in their day-to-day work.