llvm-project

History

Tobias Grosser 95935c5de1 Update matmul example to the latest polly version As the namings of the scops have changed, polly was not able to read in the user given .jscop files. By renaming the provided files, polly now finds them again and can use them to optimize the matmul function. We also update the generated files to reflect the very latest version of Polly. llvm-svn: 182265		2013-05-20 14:01:54 +00:00
..
autoconf	'chmod -x' on files that do not need the executable bits	2012-12-29 15:09:03 +00:00
cmake	autoconf/cmake: Always require isl code generation.	2012-10-21 21:48:21 +00:00
docs	…
include	LoopGenerators: Construct loops such that they are already loop rotated	2013-05-16 06:40:06 +00:00
lib	Update LoopInfo correctly	2013-05-16 06:40:24 +00:00
test	rename make check target to match the naming convention followed in the other llvm projects	2013-05-17 23:04:28 +00:00
tools	Reformat with clang-format	2013-05-07 07:30:56 +00:00
utils	cmake: Add target to reformat with clang-format	2013-05-07 07:30:31 +00:00
www	Update matmul example to the latest polly version	2013-05-20 14:01:54 +00:00
CMakeLists.txt	cmake: Add target to reformat with clang-format	2013-05-07 07:30:31 +00:00
CREDITS.txt	(Test commit for polly)	2011-07-16 13:30:03 +00:00
LICENSE.txt	Update the copyright coredits -- Happy new year 2013!	2013-01-01 10:00:19 +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	'chmod -x' on files that do not need the executable bits	2012-12-29 15:09:03 +00:00
Makefile.config.in	'chmod -x' on files that do not need the executable bits	2012-12-29 15:09:03 +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.