Just optimizing the "hotspots" is not the only strategy to improving performance. As Daniel alludes to towards the end, it's about maximizing effort versus payo

Just optimizing the "hotspots" is not the only strategy to improving performance. As Daniel alludes to towards the end, it's about maximizing effort versus payoff. E.g. you might find a hotspot that indicates 20% of the time is spent in a very small function. Yet that small function might be already close to optimal. After intense effort, you improve this function by 25%. Great, you saved 5% overall. But there might be a 5% somewhere else in the program that shouldn't exist at all, and if you remove it, you also get a 5% speedup. Another example is that maybe you have a loop around 10 function calls that calls each of them 5 times. Each of them might show up as 10% of the execution time. But then you discover that the entire loop only needs to execute once. So you can speed up the program by 5x by only iterating once. Yet the proportion in the profile doesn't change; the program just does 5x less work. Profiles can be deceiving.