llvm-project/polly
John Ericson df31ff1b29 [cmake] Make include(GNUInstallDirs) always below project(..)
Its defaulting logic must go after `project(..)` to work correctly,  but `project(..)` is often in a standalone condition making this
awkward, since the rest of the condition code may also need GNUInstallDirs.

The good thing is there are the various standalone booleans, which I had missed before. This makes splitting the conditional blocks less awkward.

Reviewed By: arichardson, phosek, beanz, ldionne, #libunwind, #libc, #libc_abi

Differential Revision: https://reviews.llvm.org/D117639
2022-01-20 18:59:17 +00:00
..
cmake [polly][cmake] Use `GNUInstallDirs` to support custom installation dirs 2022-01-18 20:33:42 +00:00
docs [NFC] Fix typos in release notes. 2021-12-14 14:19:42 -08:00
include/polly [SCEV] Sequential/in-order `UMin` expression 2022-01-10 20:51:26 +03:00
lib [polly][cmake] Use `GNUInstallDirs` to support custom installation dirs 2022-01-18 20:33:42 +00:00
test [IRBuilder] Migrate and-folding to value-based FoldAnd. 2022-01-20 10:22:21 +00:00
tools
unittests [Polly][Isl] Use the function unsignedFromIslSize to manage a isl::size object. NFCI 2021-11-05 11:15:22 +01:00
utils Harmonize Python shebang 2020-07-16 21:53:45 +02:00
www [Polly] Clean up Polly's getting started docs. 2021-10-14 12:26:57 -05:00
.arclint
.gitattributes
.gitignore
CMakeLists.txt [cmake] Make include(GNUInstallDirs) always below project(..) 2022-01-20 18:59:17 +00:00
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.