llvm-project

History

Tobias Grosser ff639688e3 www: Clarify that GMP is LGPL licensed llvm-svn: 165789		2012-10-12 07:44:38 +00:00
..
autoconf	Detect the isl code generation feature correctly	2012-10-02 19:50:22 +00:00
cmake	Detect the isl code generation feature correctly	2012-10-02 19:50:22 +00:00
docs	Add initial version of Polly	2011-04-29 06:27:02 +00:00
include	Move TargetData to DataLayout to fix build breakage caused by LLVM r16540	2012-10-08 17:26:19 +00:00
lib	Move TargetData to DataLayout to fix build breakage caused by LLVM r16540	2012-10-08 17:26:19 +00:00
test	Add test cases for multi-dimensional variable lengths arrays	2012-09-11 14:03:19 +00:00
tools	Update libGPURuntime to be dual licensed under MIT and UIUC license.	2012-07-06 10:40:15 +00:00
utils	Update isl to get the new code generation	2012-10-02 19:50:30 +00:00
www	www: Clarify that GMP is LGPL licensed	2012-10-12 07:44:38 +00:00
CMakeLists.txt	Add preliminary implementation for GPGPU code generation.	2012-08-03 12:50:07 +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	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +00:00
README	Trivial change to the README, mainly to test commit access.	2012-10-09 04:59:42 +00:00
configure	Detect the isl code generation feature correctly	2012-10-02 19:50:22 +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.