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."
That is, fiddle with settings at random until it's acceptable?
Wait, I'm confused; I thought on Slashdot we're supposed to wait for someone to mention Chess before we bring up Go?
The bold print giveth, and the fine print taketh away
Sadly, these genetic algorithms only work for blue-eyed developers
anyways think about the overkill of developing such an elegant solution for such a minor problem. i mean those few percentages of performance enhancement...
/me bows in admiration
anyway, i think it *is* really cool this guy finds it interesting to tackle this 'problem' with a genetic algorithm. why not try some bayesian networks next? support vector machines? improbability drives...
ooops, i'm getting carried away. sorry 'bout that.
(not intended as flamebait but mod me to hell anyway *evil grin*)
Neural networks != genetic algorithms
Slow Go player != inelegant solution
I was going to dissagree with you, but I'm not even sure what your arguments are. Do you have any reason to believe that the solution for the problem discussed in the article is inelegant or innefficient, or did you just get a bad grade in your AI class?
All the top of the pack human players use neural networks. Your point?
"The invisible and the non-existent look very much alike." -- Delos B. McKown
Sorry, couldn't resist
12 hours on an 8 CPU
100 million LOC, really crappy makefiles, or eight 60MHz CPUs?
Healthcare article at Kuro5hin