llvm-project

History

Hongbin Zheng ed986ab6a4 Rewritten expandRegion to clarify the intention and improve performance, patched by Johannes Doerfert <johannes@jdoerfert.de>. llvm-svn: 154260		2012-04-07 15:14:28 +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	CodeGen: Extract the LLVM-IR generaction of scalar and OpenMP loops.	2012-03-23 10:35:18 +00:00
lib	Rewritten expandRegion to clarify the intention and improve	2012-04-07 15:14:28 +00:00
test	CodeGen: Allow Polly to do 'grouped unrolling', but no vector generation.	2012-04-07 06:16:08 +00:00
tools	Add initial version of Polly	2011-04-29 06:27:02 +00:00
utils	Update isl	2012-02-20 08:41:44 +00:00
www	www: Fix typo, replace "LD_LBIRARY_PATH" by "LD_LIBRARY_PATH" in get_started.	2012-04-03 09:15:52 +00:00
CMakeLists.txt	Out of tree build support: Set TARGET_TRIPLE from the result of "llvm-config --host-target"	2012-03-27 07:56:07 +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	Fix a bug introduced by r153739: We are not able to provide the correct	2012-04-06 03:56:27 +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.