llvm-project

History

Tobias Grosser 0c55514a43 autoconf/cmake: Always require isl code generation. This change ensures that isl is only detected if it includes code generation support. This allows us to remove a lot of conditional compilation and also avoids missing test cases in case the feature is not available. llvm-svn: 166403		2012-10-21 21:48:21 +00:00
..
autoconf	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +00:00
cmake	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +00:00
docs	Add initial version of Polly	2011-04-29 06:27:02 +00:00
include	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +00:00
lib	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +00:00
test	cmake: Use suffix for shared modules instead of the one for shared libraries	2012-10-21 21:08:29 +00:00
tools	Update libGPURuntime to be dual licensed under MIT and UIUC license.	2012-07-06 10:40:15 +00:00
utils	isl scheduler: Do not fail when returning an empty band list	2012-10-16 07:29:19 +00:00
www	www: Correct command line that loads polly into dragonegg	2012-10-21 17:33:00 +00:00
CMakeLists.txt	Introduce a separate file for CMake macros	2012-10-21 15:51:49 +00:00
CREDITS.txt	(Test commit for polly)	2011-07-16 13:30:03 +00:00
LICENSE.txt	Happy new year 2012!	2012-01-01 08:16:56 +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	Add initial version of Polly	2011-04-29 06:27:02 +00:00
Makefile.config.in	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +00:00
README	Trivial change to the README, mainly to test commit access.	2012-10-09 04:59:42 +00:00
configure	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +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.