hanchenye-llvm-project/polly
Michael Kruse ea40d5b05b Moving ManagedMemoryRewritePass when hybrid option is selected
Compiling with -polly-target=hybrid was causing Polly to occur two times
in the pipeline. The reason was how the ManagedMemoryRewritePass was
registered in the pass manager. ManagedMemoryRewritePass being a
ModulePass was forcing all previous passes to get recomputed. This
commit avoids Polly to appear two times in the pipeline registering the
ManagedMemoryRewritePass later in the pass manager.

Patch by Lorenzo Chelini <l.chelini@icloud.com>

Differential Revision: https://reviews.llvm.org/D59263

llvm-svn: 356965
2019-03-25 23:26:59 +00:00
..
cmake [CMake] Fix generation of exported targets in build directory 2018-11-06 15:18:17 +00:00
docs [CodeGen] LLVM OpenMP Backend. 2019-03-19 03:18:21 +00:00
include/polly [CodeGen] LLVM OpenMP Backend. 2019-03-19 03:18:21 +00:00
lib Moving ManagedMemoryRewritePass when hybrid option is selected 2019-03-25 23:26:59 +00:00
test [CodeGen] LLVM OpenMP Backend. 2019-03-19 03:18:21 +00:00
tools Fix typos throughout the license files that somehow I and my reviewers 2019-01-21 09:52:34 +00:00
unittests Update the file headers across all of the LLVM projects in the monorepo 2019-01-19 08:50:56 +00:00
utils [arc] Remove unittesting from arcconfig 2018-05-15 13:43:42 +00:00
www Adjust documentation for git migration. 2019-01-29 16:37:27 +00:00
.arcconfig [arc] Remove unittesting from arcconfig 2018-05-15 13:43:42 +00:00
.arclint
.gitattributes
.gitignore
CMakeLists.txt [JSONExporter] Replace bundled Jsoncpp with llvm/Support/JSON.h. NFC. 2018-08-01 00:15:16 +00:00
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.