llvm-capstone/polly
Tobias Hieta f98ee40f4b
[NFC][Py Reformat] Reformat python files in the rest of the dirs
This is an ongoing series of commits that are reformatting our
Python code. This catches the last of the python files to
reformat. Since they where so few I bunched them together.

Reformatting is done with `black`.

If you end up having problems merging this commit because you
have made changes to a python file, the best way to handle that
is to run git checkout --ours <yourfile> and then reformat it
with black.

If you run into any problems, post to discourse about it and
we will try to help.

RFC Thread below:

https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style

Reviewed By: jhenderson, #libc, Mordante, sivachandra

Differential Revision: https://reviews.llvm.org/D150784
2023-05-25 11:17:05 +02:00
..
cmake [Polly] Remove Polly-ACC. 2023-03-08 17:33:04 -06:00
docs [NFC][Py Reformat] Reformat python files in the rest of the dirs 2023-05-25 11:17:05 +02:00
include/polly Replace None with std::nullopt in comments (NFC) 2023-05-12 18:33:26 -07:00
lib [NFC][Py Reformat] Reformat python files in the rest of the dirs 2023-05-25 11:17:05 +02:00
test [NFC][Py Reformat] Reformat python files in the rest of the dirs 2023-05-25 11:17:05 +02:00
unittests Migrate away from the soft-deprecated functions in APInt.h (NFC) 2023-02-20 00:58:29 -08:00
utils [NFC][Py Reformat] Reformat python files in the rest of the dirs 2023-05-25 11:17:05 +02:00
www [Polly] Remove Polly-ACC. 2023-03-08 17:33:04 -06:00
.arclint
.gitattributes
.gitignore
CMakeLists.txt Revert "Reland "[CMake] Bumps minimum version to 3.20.0."" 2023-05-17 10:53:33 -04:00
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.