Sunday, February 06, 2005
Genetic algorithms for tuning the Linux kernel
According to KernelTrap.org, Jake Moilanen has developed a series of four patches to introduce a genetic algorithm for automatically tuning the Linux kernel 2.6.9:
The patches update the anticipatory IO scheduler [story] and the zaphod CPU scheduler [story] 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." ...
In Jake's patch, the chromosome strings are possible tunings for Linux kernel internals, and fitness is measured in overall performance. Essentially, a large pool of possible tunings are selected (referred to above as chromosome strings). Each of these possible tunings is tested under the current workload, measured for best performance (referred to above as fitness). 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. Finally, random mutation is applied to slightly modify some of the tunings. Jake explains, "over time the tunables should converge toward the optimal settings for that workload. If the workload changes, the tunables should converge to the new optimal settings (this is part of the reason for mutation)."
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I remember John Grefenstette and some of his students at Vanderbilt University tuning operating systems (Unix,I believe) using GAs back in the mid 80s. Can't recall offhand whether the work resulted in publications, but I do recall that the GA was effective, and that offpeak times were perfect for performing the experiments on the tuned parameters.
Very interesting. Am going to try it on my linux. Just not sure which benchmark I should use. Will post some results if I successfully apply the patch.
This sort of approach could also be applied to the machine execution model of a computer architecture. That is, way, way down where strategies can influence data prefetching, memory hits, instruction loading, etc. There are many open issues though: in general, everything is happening really quickly and while a background off-line learning setup (like conventional Ev Alg) may be able to learn to recognize some sort of profile, it would be better if a strategy could quickly learning a simple model or build a simple model and learn what best to do from it. Another issue: collecting evaluation data. The interesting quirk is that one can more or less consider computation as a free resource. Like the OS level where things can be done during non-busy times, often there's on-chip resources that can be used for the tuning/learning algorithm.
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