llvm-capstone/polly
Bruno De Fraine 656bf13004
[AST] Don't merge memory locations in AliasSetTracker (#65731)
This changes the AliasSetTracker to track memory locations instead of
pointers in its alias sets. The motivation for this is outlined in an RFC
posted on LLVM discourse:
https://discourse.llvm.org/t/rfc-dont-merge-memory-locations-in-aliassettracker/73336

In the data structures of the AST implementation, I made the choice to
replace the linked list of `PointerRec` entries (that had to go anyway)
with a simple flat vector of `MemoryLocation` objects, but for the
`AliasSet` objects referenced from a lookup table, I retained the
mechanism of a linked list, reference counting, forwarding, etc. The
data structures could be revised in a follow-up change.
2024-01-17 15:59:13 +01:00
..
cmake [polly] [CMake] Create component and install target in add_polly_library (#66598) 2023-12-25 10:31:16 +00:00
docs Clear release notes for 18.x 2023-07-25 13:58:49 +02:00
include/polly [llvm][NFC] A start at cleaning up zero byte files that should have been removed (#74404) 2023-12-05 01:57:14 -05:00
lib [AST] Don't merge memory locations in AliasSetTracker (#65731) 2024-01-17 15:59:13 +01:00
test [polly][ScheduleOptimizer] Reland Fix long compile time(hang) reported in polly (#77280) 2024-01-08 09:48:02 -08:00
unittests
utils [NFC][Py Reformat] Reformat python files in the rest of the dirs 2023-05-25 11:17:05 +02:00
www [polly][www] Remove unused VideoJS 2023-09-13 12:24:46 -07:00
.arclint
.gitattributes
.gitignore
CMakeLists.txt Reland "[CMake] Bumps minimum version to 3.20.0. 2023-05-27 12:51:21 +02:00
CREDITS.txt
LICENSE.TXT
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.