[Chris_Newton]

When it becomes important, there are many technologies to mitigate the problem in your "hot loops."

There is an implicit assumption there that most of the time in your program that could be saved is spent in a small number of hot spots. This will often be true, but unfortunately it is not necessarily so.

This is a particular problem in languages like Python, which are useful (among other things) for their support for rapid prototyping and their easily readable code. All of that is lost if you can’t perform local optimizations to reach an acceptable level of performance, leaving a ground-up rewrite in a faster language like C as the next most likely strategy.

The kinds of techniques mentioned in the linked slides could help to create a middle ground that would be very useful for performance-sensitive projects that currently find themselves between a rock and a hard place.