llvm-project

History

Tobias Grosser 535afd808d ScopInfo: Check for possibly nested GEP in fixed-size delin We currently only consider the first GEP when delinearizing access functions, which makes us loose information about additional index expression offsets, which results in our SCoP model to be incorrect. With this patch we now compare the base pointers used to ensure we do not miss any additional offsets. This fixes llvm.org/PR27195. We may consider supporting nested GEP in our delinearization heuristics in the future. llvm-svn: 265379		2016-04-05 06:23:45 +00:00
..
cmake	Compile ISL into its own library	2015-09-24 11:30:22 +00:00
docs	docs: Fix section header committed in r264575	2016-03-28 17:00:14 +00:00
include/polly	Exploit graph properties during domain generation	2016-04-04 07:57:39 +00:00
lib	ScopInfo: Check for possibly nested GEP in fixed-size delin	2016-04-05 06:23:45 +00:00
test	Do not allow to complex branch conditions	2016-04-04 07:59:41 +00:00
tools	Update copyright year to 2016.	2016-03-30 22:41:38 +00:00
utils	Revise polly-{update\|check}-format targets	2015-09-14 16:59:50 +00:00
www	www: Directly link to our new SPHINX documentation	2016-03-25 14:19:34 +00:00
.arcconfig	Adjusted arc linter config for modern version of arcanist	2015-08-12 09:01:16 +00:00
.arclint	Adjusted arc linter config for modern version of arcanist	2015-08-12 09:01:16 +00:00
.gitattributes	gitattributes: .png and .txt are no text files	2013-07-28 09:05:20 +00:00
.gitignore	Add git patch files to .gitignore	2015-06-23 20:55:01 +00:00
CMakeLists.txt	cmake: Ensure tools/* is still formatted	2016-03-25 12:16:17 +00:00
CREDITS.txt	Add myself to the credits	2014-08-10 03:37:29 +00:00
LICENSE.txt	Update copyright year to 2016.	2016-03-30 22:41:38 +00:00
README	Trivial change to the README, mainly to test commit access.	2012-10-09 04:59:42 +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.