[pypy-dev] Pypy jit and (meta) genetic algorithms

David Naylor naylor.b.david at gmail.com
Tue Sep 27 22:43:20 CEST 2011

Hi All

It occurred to me that with the many options available for jit (such as 
inlining, function_threshold) there may be some merit to optimising those 
values.  I would expect that the optimised values would be workload specific 
however if a workload takes days to run then it would be worth optimising.  

I recall an article that used genetic algorithms to select the best parameters 
(for gcc) that produces the fastest execution.  Is there an equivalent program 
for pypy?  Or if it is easy enough could someone put together such a (shell 
script) program?  

I, unfortunitely, have no experience with genetic algorithms nor know how to 
optimise the jit parameters.  

-------------- next part --------------
A non-text attachment was scrubbed...
Name: signature.asc
Type: application/pgp-signature
Size: 196 bytes
Desc: This is a digitally signed message part.
URL: <http://mail.python.org/pipermail/pypy-dev/attachments/20110927/5270c920/attachment-0001.pgp>

More information about the pypy-dev mailing list