llvm-project/polly
Tobias Grosser be7eaddc69 Adjust another test case to not access out of bounds
llvm-svn: 210208
2014-06-04 19:41:47 +00:00
..
autoconf Remove OpenScop 2014-04-11 09:47:45 +00:00
cmake Remove OpenScop 2014-04-11 09:47:45 +00:00
docs
include record delinearization result and reuse it in polyhedral translation 2014-06-03 18:16:31 +00:00
lib Use range-based for loops 2014-06-04 08:06:40 +00:00
test Adjust another test case to not access out of bounds 2014-06-04 19:41:47 +00:00
tools Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
utils Update isl to fix memory bugs 2014-04-13 16:37:18 +00:00
www www: Polly can be built without any GPLed software 2014-06-04 09:26:39 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
CMakeLists.txt Reorder cmake include folders (polly source first) 2014-05-28 16:54:42 +00:00
CREDITS.txt (Test commit for polly) 2011-07-16 13:30:03 +00:00
LICENSE.txt Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +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 Remove OpenScop 2014-04-11 09:47:45 +00:00
README Trivial change to the README, mainly to test commit access. 2012-10-09 04:59:42 +00:00
configure Remove OpenScop 2014-04-11 09:47:45 +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.