llvm-project/polly
Wei Mi 86341247c4 [NFC] Rename ThinLTOPhase to ThinOrFullLTOPhase and move it from PassBuilder.h
to Pass.h.

In some compiler passes like SampleProfileLoaderPass, we want to know which
LTO/ThinLTO phase the pass is in. Currently the phase is represented in enum
class PassBuilder::ThinLTOPhase, so it is only available in PassBuilder and
it also cannot represent phase in full LTO. The patch extends it to include
full LTO phases and move it from PassBuilder.h to Pass.h, then it is much
easier for PassBuilder to communiate with each pass about current LTO phase.

Differential Revision: https://reviews.llvm.org/D94613
2021-01-13 15:55:40 -08:00
..
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 12 2020-07-15 12:05:05 +02:00
include/polly [Polly] Consider InvalidContext to determine partial READ. 2020-12-10 22:25:19 -06:00
lib [NFC] Rename ThinLTOPhase to ThinOrFullLTOPhase and move it from PassBuilder.h 2021-01-13 15:55:40 -08:00
test [IR] Let IRBuilder's CreateVectorSplat/CreateShuffleVector use poison as placeholder 2020-12-30 04:21:04 +09:00
tools Fix typos throughout the license files that somehow I and my reviewers 2019-01-21 09:52:34 +00:00
unittests [Polly] Support linking ScopPassManager against LLVM dylib 2020-08-07 06:46:35 +02:00
utils Harmonize Python shebang 2020-07-16 21:53:45 +02:00
www [BasicAA] Replace -basicaa with -basic-aa in polly 2020-06-30 15:50:17 -07:00
.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 Fix typos throughout the license files that somehow I and my reviewers 2019-01-21 09:52:34 +00:00
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.