Genetic Algorithms and Compiler Optimizations

mfago writes "Scott Robert Ladd has written an enlightening article and accompanying software package that utilizes genetic algorithms to optimize the optimization flags fed to a compiler. Those who have tried to squeeze the last drop of performance from a code know that it can be very difficult to determine the set of optimization flags beyond -O3 (with gcc for example) that yields the best performance. Lots of background links included in the article."

Comments

fragile GA, and what this is and isn't

[kisrael]

An important thing to note is that this isn't coming up with new optimizations, but rather finding the optimal mix of pre-existing gcc optimizations.

It's an important distinction. When you're trying to do breed some really new system using Genetic Algorithms, it's possible to get some very fragile results, i.e. systems that do the job incredibly efficiently, but only under certain conditions...

For instance, I remember reading some folks were trying to "breed" a circuit using Genetic Algorithm like techniques that created a "pulser" using as few gates as possible. They got something that used like one tenth the number of logic gates of the previous low record...but didn't work when they moved the setup away from their desktop PCs. Turned out what they "discovered" was a sensitive RF receiver that picked up (I think) something like the normal 120V cycling, and utilized. Similarly, a lot of the bred ciruits are extremely sensitive to temperature and other conditions.

So breeding for efficiency can be a bit dangerous, you get things that rely on odd side-effects and environmental states. Though I think the method the article describes isn't much more dangerous than normal optimization selection, though admittedly more likely to stumble over some unusual flag combination that might break under certain circumstances.

In short, *if* GA is ever going to let software development become more like gardening than engineering, (something I've herd as a goal) we're going to have to find ways to apply very general evolutionary pressures, complex test environments.