llvm-project

History

Tobias Grosser c1b6cec0e7 Make polly -Wdocumentation clean llvm-svn: 168311		2012-11-19 12:26:25 +00:00
..
autoconf	autoconf: isl depends on gmp include files	2012-11-15 21:20:22 +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	Make polly -Wdocumentation clean	2012-11-19 12:26:25 +00:00
lib	Make polly -Wdocumentation clean	2012-11-19 12:26:25 +00:00
test	test: LLVM supports now vectors of arbitrary pointers	2012-11-14 08:25:52 +00:00
tools	Update libGPURuntime to be dual licensed under MIT and UIUC license.	2012-07-06 10:40:15 +00:00
utils	utils: use rmdir instead of rm to remove empty dirs	2012-11-18 04:34:31 +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: isl depends on gmp include files	2012-11-15 21:20:22 +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.