llvm-project/polly
Tobias Grosser 4071cb571a [ScopInfo] Translate getNonHoistableCtx to C++ [NFC]
llvm-svn: 304841
2017-06-06 23:13:02 +00:00
..
cmake [CMake] Retire Polly's FindCUDA.cmake in favour of CMake's default FindCUDA.cmake script. 2017-06-06 19:20:48 +00:00
docs Update information on isl C++ bindings in Polly release notes 2017-05-27 11:01:01 +00:00
include/polly [ScopInfo] Translate getNonHoistableCtx to C++ [NFC] 2017-06-06 23:13:02 +00:00
lib [ScopInfo] Translate getNonHoistableCtx to C++ [NFC] 2017-06-06 23:13:02 +00:00
test [Simplify] Use execution order of memory accesses. 2017-06-06 17:46:42 +00:00
tools [CMake] Retire Polly's FindCUDA.cmake in favour of CMake's default FindCUDA.cmake script. 2017-06-06 19:20:48 +00:00
unittests [Polly][NewPM] Reenable ScopPassManager unittest 2017-05-23 11:28:50 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www [www] Fix links to bug tracker 2017-06-06 06:23:20 +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] Retire Polly's FindCUDA.cmake in favour of CMake's default FindCUDA.cmake script. 2017-06-06 19:20:48 +00:00
CREDITS.txt
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.