llvm-project/polly
Siddharth Bhat 88619946b6 [CUDA Managed Memory] Fix regression introduced by Managed Memory
- Fixes breakage from commit 5536f.
- Interference with commit 764f3 caused testcase to fail. Reverting
  764f3 allows commit 5536f to succeed.
- Generated kernel code was slightly different due to 764f3, which
  caused testcase to fail.

llvm-svn: 302021
2017-05-03 13:15:27 +00:00
..
cmake [Polly][Cmake] Add missing include paths to exported cmake config 2017-04-27 16:03:42 +00:00
docs Porting the example illustrating Polly from HTML to reStructuredText 2017-02-10 11:46:57 +00:00
include/polly [ScopInfo] Remove code not needed anymore after r302004 2017-05-03 08:02:32 +00:00
lib [ScopInfo] Remove code not needed anymore after r302004 2017-05-03 08:02:32 +00:00
test [CUDA Managed Memory] Fix regression introduced by Managed Memory 2017-05-03 13:15:27 +00:00
tools [Polly] [PPCGCodeGeneration] Add managed memory support to GPU code 2017-04-28 11:16:30 +00:00
unittests [CMake] Use object library to build the two flavours of Polly. 2017-04-27 16:13:03 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www Add two Polly images 2017-04-05 11:50:31 +00:00
.arcconfig Upgrade all the .arcconfigs to https. 2016-07-14 13:15:37 +00:00
.arclint [External] Move lib/JSON to lib/External/JSON. NFC. 2017-02-05 15:26:56 +00:00
.gitattributes
.gitignore Do not track the isl PDF manual in SVN 2017-01-16 11:48:03 +00:00
CMakeLists.txt [CMake] Use object library to build the two flavours of Polly. 2017-04-27 16:13:03 +00:00
CREDITS.txt Add myself to the credits 2014-08-10 03:37:29 +00:00
LICENSE.txt [External] Move lib/JSON to lib/External/JSON. NFC. 2017-02-05 15:26:56 +00:00
README

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.