|Machine Learning in Optimization ? email@example.com (Stephan Ceram) (2008-06-02)|
|Re: Machine Learning in Optimization ? firstname.lastname@example.org (=?ISO-8859-1?Q?Bj=F6rn_Franke?=) (2008-06-04)|
|Re: Machine Learning in Optimization ? email@example.com (Stephan Ceram) (2008-06-09)|
|Re: Machine Learning in Optimization ? firstname.lastname@example.org (=?ISO-8859-1?Q?Bj=F6rn_Franke?=) (2008-06-10)|
|From:||Stephan Ceram <email@example.com>|
|Date:||2 Jun 2008 16:52:58 GMT|
|Posted-Date:||02 Jun 2008 13:56:22 EDT|
I've read the paper "Using Machine Learning to Focus Iterative
Optimization" where the authors use machine learning to learn "good"
sequences of compiler transformations for particular classes of
programs. They then use the learned data as start sequences for new
programs to run an focused iterative compiler optimization based on a
genetic algorithm. That way they don't waste time with "worse"
Just out of curiosity I was wondering how the machine learning system
is integrated into a compiler. Is this done by having a data base
which is extended during learning or are the results stored in a file?
And what free tools are there which can be integrated in a C++
project? Any powerful C++ libraries?
I'd be grateful to hear your experiences.
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