llvm-project/polly
Tobias Grosser c393ff07b2 Dependences: Refine the compute out facility
We update to a newer version of isl, which includes changes to the compute
out facility that make it a lot more predicable. With our new value, we can
reliably bail out for all reported bugs, while still being able to compute
dependences for all but two test cases in the LLVM test suite. For the remaining
two test cases, the dependence problem we construct is unnecessarily complex,
so there is hope we can improve on this. However, to avoid any future issues,
having a reliable compute out facility in place is important.

llvm-svn: 206106
2014-04-12 11:39:28 +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 Dependences: Refine the compute out facility 2014-04-12 11:39:28 +00:00
test only delinearize when the access function is not affine 2014-04-10 16:08:11 +00:00
tools Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
utils Dependences: Refine the compute out facility 2014-04-12 11:39:28 +00:00
www Remove OpenScop 2014-04-11 09:47:45 +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.