llvm-project

History

Tobias Grosser e275e9216b Return conservative result in case the dependence check timed out For complex examples it may happen that we do not compute dependences. In this case we do not want to crash, but just not detect parallel loops. llvm-svn: 204470		2014-03-21 15:12:09 +00:00
..
autoconf	GMP is only required for CLooG	2014-02-22 02:15:39 +00:00
cmake	record in POLLY_LINK_LIBS all the libs needed for polly	2014-03-13 20:24:48 +00:00
docs	…
include	clang-format: Remove empty lines	2014-03-21 14:04:25 +00:00
lib	Return conservative result in case the dependence check timed out	2014-03-21 15:12:09 +00:00
test	Return conservative result in case the dependence check timed out	2014-03-21 15:12:09 +00:00
tools	Update the copyright credits -- Happy new year 2014!	2014-01-01 08:27:31 +00:00
utils	Update CLooG and some test cases	2014-03-10 17:31:22 +00:00
www	www: More formatting improvements	2014-03-21 13:38:02 +00:00
.gitattributes	gitattributes: .png and .txt are no text files	2013-07-28 09:05:20 +00:00
CMakeLists.txt	record in POLLY_LINK_LIBS all the libs needed for polly	2014-03-13 20:24:48 +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	autoconf: Add PLUTO_FOUND flag	2014-03-18 18:50:58 +00:00
README	Trivial change to the README, mainly to test commit access.	2012-10-09 04:59:42 +00:00
configure	GMP is only required for CLooG	2014-02-22 02:15:39 +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.