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llvm-project/polly
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Andreas Simbuerger 06904d8554 Do not use namespace polly inside a header. 
In general this fixes ambiguity that can arise from using
a different namespace that declares the same symbols as
we do.

One example inside llvm would be:
  createIndVarSimplifyPass(..);

Which can be found in:
  llvm/Transforms/Scalar.h
and
  polly/LinkAllPasses.h

llvm-svn: 210755
 		2014-06-12 07:26:25 +00:00
..
autoconf Remove OpenScop 2014-04-11 09:47:45 +00:00
cmake Remove OpenScop 2014-04-11 09:47:45 +00:00
docs
…
include Do not use namespace polly inside a header. 2014-06-12 07:26:25 +00:00
lib Do not use namespace polly inside a header. 2014-06-12 07:26:25 +00:00
test Test delinearization of 2D diagonal matrix 2014-06-10 14:48:17 +00:00
tools Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
utils Update isl to fix memory bugs 2014-04-13 16:37:18 +00:00
www www: Fix grammar. 2014-06-10 20:18:16 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
CMakeLists.txt Reorder cmake include folders (polly source first) 2014-05-28 16:54:42 +00:00
CREDITS.txt (Test commit for polly) 2011-07-16 13:30:03 +00:00
LICENSE.txt Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
Makefile Revert "Fix a bug introduced by r153739: We are not able to provide the correct" 2012-04-11 07:43:13 +00:00
Makefile.common.in 'chmod -x' on files that do not need the executable bits 2012-12-29 15:09:03 +00:00
Makefile.config.in Remove OpenScop 2014-04-11 09:47:45 +00:00
README Trivial change to the README, mainly to test commit access. 2012-10-09 04:59:42 +00:00
configure Remove OpenScop 2014-04-11 09:47:45 +00:00

README 

Polly - Polyhedral optimizations for LLVM
-----------------------------------------
http://polly.llvm.org/

Polly uses a mathematical representation, the polyhedral model, to represent and
transform loops and other control flow structures. Using an abstract
representation it is possible to reason about transformations in a more general
way and to use highly optimized linear programming libraries to figure out the
optimal loop structure. These transformations can be used to do constant
propagation through arrays, remove dead loop iterations, optimize loops for
cache locality, optimize arrays, apply advanced automatic parallelization, drive
vectorization, or they can be used to do software pipelining.