llvm-project/polly
Michael Kruse 286677870b [Polly][ManualOpt] Match interpretation of unroll metadata to LoopUnrolls's.
We previously had a different interpretation of unroll transformation
attributes than how LoopUnroll interpreted it. In particular,
llvm.loop.unroll.enable was needed explicitly to enable it and disabling
metadata was ignored.
Additionally, it required that either full unrolling or an unroll factor
to be specified or fail otherwise. An unroll factor is still required,
but the transformation is ignored with the hope that LoopUnroll is going
to apply the unrolling, since Polly currently does not implement an
heuristic.

Fixes llvm.org/PR50109
2021-04-24 04:30:19 -05:00
..
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 13 2021-01-26 19:37:55 -08:00
include/polly [Polly][ManualOpt] Match interpretation of unroll metadata to LoopUnrolls's. 2021-04-24 04:30:19 -05:00
lib [Polly][ManualOpt] Match interpretation of unroll metadata to LoopUnrolls's. 2021-04-24 04:30:19 -05:00
test [Polly][ManualOpt] Match interpretation of unroll metadata to LoopUnrolls's. 2021-04-24 04:30:19 -05:00
tools Fix typos throughout the license files that somehow I and my reviewers 2019-01-21 09:52:34 +00:00
unittests [Polly] Regenerate isl-noexceptions.h. 2021-02-14 19:17:54 -06:00
utils Harmonize Python shebang 2020-07-16 21:53:45 +02:00
www [Branch-Rename] Fix some links 2021-02-01 16:43:21 +05:30
.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 Rename top-level LICENSE.txt files to LICENSE.TXT 2021-03-10 21:26:24 -08:00
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.