llvm-project

History

Tobias Grosser 1d348673e0 Add a sceleton for a polyhedral dead code elimination. Such a dead code elimination can remove redundant stores to arrays. It can also eliminate calculations where the results are stored to memory but where they are overwritten before ever being read. It may also fix bugs like: http://llvm.org/bugs/show_bug.cgi?id=5117 This commit just adds a sceleton without any functionality. If anybody is interested to learn about polyhedral optimizations this would be a good task. Well definined, self contained and pretty simple. Ping me if you want to start and you need some pointers to get going. llvm-svn: 149386		2012-01-31 14:00:27 +00:00
..
autoconf	configure: Add gmp_inc when checking for CLooG	2011-10-04 06:55:03 +00:00
cmake	Add initial version of Polly	2011-04-29 06:27:02 +00:00
docs	Add initial version of Polly	2011-04-29 06:27:02 +00:00
include	Add a sceleton for a polyhedral dead code elimination.	2012-01-31 14:00:27 +00:00
lib	Add a sceleton for a polyhedral dead code elimination.	2012-01-31 14:00:27 +00:00
test	Support non-affine access functions in Polly.	2011-12-20 10:43:14 +00:00
tools	Add initial version of Polly	2011-04-29 06:27:02 +00:00
utils	Use isl version: 3c66541593a6bf3b5a3d35d31567abe6c9e5a04b	2012-01-30 19:38:40 +00:00
www	www: Add news about the improved isl scheduling support	2012-01-31 09:13:12 +00:00
CMakeLists.txt	Buildsystem: Add -no-rtti	2011-06-30 19:50:04 +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	Add initial version of Polly	2011-04-29 06:27:02 +00:00
Makefile.common.in	Add initial version of Polly	2011-04-29 06:27:02 +00:00
Makefile.config.in	Buildsystem: Add -no-rtti	2011-06-30 19:50:04 +00:00
README	Remove some empty lines	2011-10-04 06:56:36 +00:00
configure	configure: Add gmp_inc when checking for CLooG	2011-10-04 06:55:03 +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.