llvm-capstone/polly
Michael Kruse 23753c6088 [Polly] Hide Simplify implementation from header. NFC.
Move SimplifiyVisitor from Simplify.h to Simplify.cpp. It is not
relevant for applying the pass in either the NewPM or the legacyPM.
Rename it to SimplifyImpl to account for that.

This is possible due its state not being necessary to be preserved
between runs and thefore SimplifyImpl not needed to be held in the
pass object. Instead, SimplifyImpl is only instatiated for the
current Scop. In the NewPM as a function-local variable, and in the
legacy PM inside a llvm::Optional object because the state must be
preserved between the printScop (invoked by opt -analyze) and the most
recent runOnScop calls.
2021-02-10 22:11:52 -06:00
..
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] Hide Simplify implementation from header. NFC. 2021-02-10 22:11:52 -06:00
lib [Polly] Hide Simplify implementation from header. NFC. 2021-02-10 22:11:52 -06:00
test [Polly] Added dedicated test for working -O3 pipeline. 2021-02-10 13:25:56 -06:00
tools
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 [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
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.