hanchenye-llvm-project/polly
Roman Gareev 23df27682a Map the new load to the base pointer of the invariant load hoisted load
Map the new load to the base pointer of the invariant load hoisted load
to be able to find the alias information for it.

Reviewed-by: Tobias Grosser <tobias@grosser.es>

Differential Revision: https://reviews.llvm.org/D30605

llvm-svn: 298507
2017-03-22 13:57:53 +00:00
..
cmake [Cmake] Generate a PollyConfig.cmake. 2017-03-09 17:58:20 +00:00
docs Porting the example illustrating Polly from HTML to reStructuredText 2017-02-10 11:46:57 +00:00
include/polly [CodeGen] Remove need for all parameters to be in scop context for load hoisting. 2017-03-18 23:12:49 +00:00
lib Map the new load to the base pointer of the invariant load hoisted load 2017-03-22 13:57:53 +00:00
test Map the new load to the base pointer of the invariant load hoisted load 2017-03-22 13:57:53 +00:00
tools GPURuntime: ensure compilation with C99 2016-09-11 07:32:50 +00:00
unittests [DeLICM] Refector out parseSetOrNull. NFC. 2017-03-20 15:37:32 +00:00
utils
www Porting the example illustrating Polly from HTML to reStructuredText 2017-02-10 11:46:57 +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] Generate a PollyConfig.cmake. 2017-03-09 17:58:20 +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.