llvm-project/polly
Michael Kruse ad84c6f657 [polly] Match function definitions and header declarations. NFC.
Ensure that function definitions match their declrations in header
files, even if they have no effect on linking. This includes

 1. Both have the same __isl_* annotations

 2. Both use the same type alias

 3. Remove unused declarations that have no definition

 4. Use explicit polly namespace qualifier for definitions; generally,
    the .cpp file should use at most an anon namespace region since
    only symbols declared in the header file can be accessed from other
    translation units anyway. For defintions that have been declared in
    the header file, the explicit namespace qualifier ensures that both
    match.
2022-02-16 12:52:17 -06:00
..
cmake [polly][cmake] Use `GNUInstallDirs` to support custom installation dirs 2022-01-18 20:33:42 +00:00
docs Bump the trunk major version to 15 2022-02-01 23:54:52 -08:00
include/polly [polly] Match function definitions and header declarations. NFC. 2022-02-16 12:52:17 -06:00
lib [polly] Match function definitions and header declarations. NFC. 2022-02-16 12:52:17 -06:00
test [SCEV] `createNodeForSelectOrPHIInstWithICmpInstCond()`: generalize eq handling 2022-02-11 21:58:19 +03:00
tools
unittests [polly][unittests] Link DeLICMTests with libLLVMCore 2022-01-28 21:58:40 +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.