hanchenye-llvm-project/polly
Nikita Popov 4df8efce80 [AA] Split up LocationSize::unknown()
Currently, we have some confusion in the codebase regarding the
meaning of LocationSize::unknown(): Some parts (including most of
BasicAA) assume that LocationSize::unknown() only allows accesses
after the base pointer. Some parts (various callers of AA) assume
that LocationSize::unknown() allows accesses both before and after
the base pointer (but within the underlying object).

This patch splits up LocationSize::unknown() into
LocationSize::afterPointer() and LocationSize::beforeOrAfterPointer()
to make this completely unambiguous. I tried my best to determine
which one is appropriate for all the existing uses.

The test changes in cs-cs.ll in particular illustrate a previously
clearly incorrect AA result: We were effectively assuming that
argmemonly functions were only allowed to access their arguments
after the passed pointer, but not before it. I'm pretty sure that
this was not intentional, and it's certainly not specified by
LangRef that way.

Differential Revision: https://reviews.llvm.org/D91649
2020-11-26 18:39:55 +01:00
..
cmake [Windows][Polly] Disable LLVMPolly module for all compilers on Windows 2020-09-15 09:12:38 +03:00
docs
include/polly [Polly] Move SimplifyVisitor into polly namespace. 2020-11-16 18:59:08 -06:00
lib [AA] Split up LocationSize::unknown() 2020-11-26 18:39:55 +01:00
test [Polly][OpTree] Fix mid-processing change of access kind. 2020-11-11 16:21:48 -06:00
tools
unittests [Polly] Support linking ScopPassManager against LLVM dylib 2020-08-07 06:46:35 +02:00
utils
www
.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.