hanchenye-llvm-project/polly
Michael Kruse 6249bfeefe [Polly][CodeGen] Remove use of ScalarEvolution.
ScalarEvolution::getSCEV cannot be used during codegen. ScalarEvolution
assumes a stable IR and control flow which is under construction during
Polly's CodeGen. In particular, it uses DominatorTree for compute the
backedge taken count. However the DominatorTree is not updated during
codegen.

In this case, SCEV was used to determine the base pointer of an array
access. Replace it by our own function. Polly generates only GEP and
BitCasts for array acceses, i.e. it is sufficient to handle these to to
find the base pointer.

Fixes llvm.org/PR48422
2020-12-07 15:21:51 -06:00
..
cmake [Windows][Polly] Disable LLVMPolly module for all compilers on Windows 2020-09-15 09:12:38 +03:00
docs Bump the trunk major version to 12 2020-07-15 12:05:05 +02:00
include/polly [NFC] Reduce include files dependency. 2020-12-03 18:25:05 +03:00
lib [Polly][CodeGen] Remove use of ScalarEvolution. 2020-12-07 15:21:51 -06:00
test [Polly][CodeGen] Remove use of ScalarEvolution. 2020-12-07 15:21:51 -06:00
tools
unittests [Polly] Support linking ScopPassManager against LLVM dylib 2020-08-07 06:46:35 +02:00
utils Harmonize Python shebang 2020-07-16 21:53:45 +02:00
www [BasicAA] Replace -basicaa with -basic-aa in polly 2020-06-30 15:50:17 -07:00
.arclint
.gitattributes
.gitignore
CMakeLists.txt Remove .svn from exclude list as we moved to git 2020-10-21 16:09:21 +02:00
CREDITS.txt
LICENSE.txt
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.