hanchenye-llvm-project/polly
Andy Gibbs 9936b214c0 Integrate polly test-suite into an llvm "make check-all" if built as part of the whole using cmake.
llvm-svn: 169487
2012-12-06 07:59:18 +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 Integrate polly test-suite into an llvm "make check-all" if built as part of the whole using cmake. 2012-12-06 07:59:18 +00:00
tools Update libGPURuntime to be dual licensed under MIT and UIUC license. 2012-07-06 10:40:15 +00:00
utils User isl sha commit id instead of the git tag 2012-12-04 21:54:37 +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.