llvm-project

History

Tobias Grosser b2e572c6df Update the recommended isl version Recent changes in isl: - Allow analysis of loops during code generation This simplifies the detection of parallel loops. - Simplify the way costumized ast printers are defined This enables us to highlight parallel / vector loops in our debug output. - Compile time improvements for codegen contexts that include parameters - Various bug fixes This update also gets us in sync for the isl 0.11 release. llvm-svn: 169100		2012-12-01 21:51:10 +00:00
..
autoconf	do not require cloog from configure	2012-11-26 23:03:41 +00:00
cmake	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +00:00
docs	…
include	remove dependence on CLOOG_FOUND for PollyVectorizerChoice	2012-11-26 22:16:17 +00:00
lib	Update the recommended isl version	2012-12-01 21:51:10 +00:00
test	Fix tests with broken datalayout strings.	2012-11-28 13:30:31 +00:00
tools	Update libGPURuntime to be dual licensed under MIT and UIUC license.	2012-07-06 10:40:15 +00:00
utils	Update the recommended isl version	2012-12-01 21:51:10 +00:00
www	www: Correct command line that loads polly into dragonegg	2012-10-21 17:33:00 +00:00
CMakeLists.txt	cmake: Fix installation of include files	2012-11-28 10:12:21 +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	…
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	do not require cloog from configure	2012-11-26 23:03:41 +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.