Hey,

I'm a CS Master's student at Cal Poly Pomona focusing on programming language 
theory & implementation -- particularly optimizations.  Particularly 
optimization "frameworks": ways of simplifying compilers' often repetitive, 
ad-hoc code into a single conceptual model.

This summer, I've been reading the source of Factor's compiler on again / off 
again.  I'm most of the way through compiler/tree (I've read up through 
escape-analysis in depth), though I've given all of the code many look-overs.  
I wouldn't dare call myself an expert in Factor, but reading it has become 
pretty natural and I'm not at that "I hear it's good for pushing stacks, but 
you can't do variables" phase. :)

This upcoming quarter (Fall 2010-11) I'm working on my thesis, and one paper 
keeps getting stuck in my mind: Composing Dataflow Analyses and Transformations 
by Lerner, Grove, and Chambers ( 
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.6534 ).  It presents 
a unified way of writing dataflow optimizations so they automatically compose 
together in mutually beneficial ways, even though they're defined separately 
with no special knowledge of each other.  It's the stuff behind the GHC guys' 
Hoopl framework, which is apparently being merged into their new backend: 
http://www.cs.tufts.edu/~nr/pubs/dfopt-abstract.html (a newer version is linked 
there, too).

With their powers combined, they form! ...an idea my thesis advisor seemed 
receptive to.  Would overhauling the dataflow analyses & optimizations of the 
Factor compiler be useful or interesting to you guys, even just to see the 
results?  Or am I way off base here?  If so, any better ideas?  I'm finding 
polyhedral optimization interesting, but if the guys at GCC and LLVM take such 
a long time coding their frameworks, I can only imagine how terribly I'd do.  :P

Thanks for your feedback,

-- Alex Vondrak
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