llvm-project

History

Tobias Grosser 9e97ae143f Update llvm.codegen() patch for CodeGen.cpp changes in r159694. Contributed by: Yabin Hu <yabin.hwu@gmail.com> llvm-svn: 161160		2012-08-02 08:16:40 +00:00
..
autoconf	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +00:00
cmake	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +00:00
docs	Add initial version of Polly	2011-04-29 06:27:02 +00:00
include	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +00:00
lib	Add missing dependency to cmake system	2012-08-02 07:47:37 +00:00
test	IndependentBLocks: Do not visit the same instruction twice when moving the	2012-08-01 08:46:11 +00:00
tools	Update libGPURuntime to be dual licensed under MIT and UIUC license.	2012-07-06 10:40:15 +00:00
utils	Update llvm.codegen() patch for CodeGen.cpp changes in r159694.	2012-08-02 08:16:40 +00:00
www	Create a new directory before running the polly script	2012-07-24 16:58:57 +00:00
CMakeLists.txt	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +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	Remove some empty lines	2011-10-04 06:56:36 +00:00
configure	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +00:00

README

Polly - Polyhedral optimizations for LLVM

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.