

Machine learning revolutionizes software development - cageface
http://www.sciencedaily.com/releases/2010/04/100420161222.htm

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henning
Revolutions in software development are pretty rare. Developing for the web
probably qualifies. Mainstream acceptance of garbage collection as well,
probably. This seems overblown.

How is this different from tools like Acovea?
<http://www.coyotegulch.com/products/acovea/>

More of a search problem than a learning problem if that's what's really going
on.

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skybrian
Much better explanation here:
<http://ctuning.org/wiki/index.php/CTools:MilepostGCC>

The basic idea is to use machine learning to pick the best set of gcc compiler
options for a program. A better optimizer for a compiler is useful but it's
hardly a revolution.

Edit: to clarify, this isn't just choosing command-line flags, but using
machine learning in the compiler's internals.

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keefe
little bit of sensationalism here...

"compiler optimisation has become a bottleneck in the development process"

Certainly not true for arbitrary development.

"an automatic way to optimise compilers for re-configurable embedded
processors"

and they are working on a particular special case...

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typedef_void
How is this better than JITting?

It seems like most recent performance gains come from runtime/dynamic
optimizations, not static/compile time optimizations.

~~~
eru
> It seems like most recent performance gains come from runtime/dynamic
> optimizations, not static/compile time optimizations.

Are you sure? Have you heard of stream fusion for Haskell?

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Dilpil
Ten times faster? Ten times? I find that completely implausible.

