Tuning The Kernel With A Genetic Algorithm

fsck! writes "Jake Moilanen provided a series of four patches against the 2.6.9 Linux kernel that introduce a simple genetic algorithm used for automatic tuning. The patches update the anticipatory IO scheduler and the zaphod CPU scheduler to both use the new in-kernel library, theoretically allowing them to automatically tune themselves for the best possible performance for any given workload. Jake says, 'using these patches, there are small gains (1-3%) in Unixbench & SpecJBB. I am hoping a scheduler guru will able to rework them to give higher gains.'"

Comments

Re:Dear Kernel Coders

[Xpilot]

Go grab the patches. They're commited into the BK repositories already. Sheesh.

Patches for 2.4 can be found in this changeset.

Patches for 2.6 can be found in this changeset.

Click on the little "diff -Nur style" link for a an actual usable patch.

In the course of a few hours, you have the fixes already. Yay for open source.

Btw, nice troll :p

Re:Complexity?

[grammar fascist]

Understanding (basic of) GAs is easy and so is implementation.

I'm a machine learning researcher, and I'll second this. Also, the code for it will be quite self-contained (if done right), and shouldn't affect any parts of the kernel except where it's called to run an iteration.

They also work quite well. That's why they are so popular.

Sure they do. For a lot of problems, though, they're not so hot compared to other optimization methods like hill climbing and empirical gradient descent - they tend to run slowly - and I would like to ask Mr. Moilanen why he didn't use one. GAs are especially good with nominal parameters (discrete, unordered). But I would expect tuning parameters to be either discrete or continuous.

GAs are theoretically capable of finding global optima, except that's kind of a red herring. The only reason you can prove that is that, theoretically, no matter how small the probability, you'll eventually get exactly the right sequence of mutations to produce a global optimum. In practice, they tend to produce local optima like most other optimization algorithms - especially as Moilanen describes it:

All of the tunings are then ordered from best performance to worst, and the worst half of the performers are replaced with new possible tunings derived from the best half of the performers.

You generally have to be a little less selective (more random) than this.