llvm-project/polly
Tobias Grosser 954939842f Really fix the load case.
Commit r206510 falsely advertised to fix the load cases, even though it only
fixed the store case. This commit adds the same fix for the load case including
the missing test coverage.

llvm-svn: 206577
2014-04-18 09:46:35 +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 Remove OpenScop 2014-04-11 09:47:45 +00:00
lib Really fix the load case. 2014-04-18 09:46:35 +00:00
test Really fix the load case. 2014-04-18 09:46:35 +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 Link to notes of our last phone call 2014-04-17 19:59:38 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
CMakeLists.txt Remove OpenScop 2014-04-11 09:47:45 +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.