hanchenye-llvm-project/polly
Tim Northover 4bc20fa149 OpaquePtr: Update polly's calls to Loads.h API
The Loads.h API changed so that a Type parameter is now mandatory in
preparation for pointer types being opaque. Unfortunately I don't build
polly routinely and it still had some uses. This just provides the
(obvious) load type in each case.

llvm-svn: 365470
2019-07-09 12:13:31 +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 [ScopBuilder] Move addInvariantLoads to ScopBuilder. NFC. 2019-06-12 22:51:56 +00:00
lib OpaquePtr: Update polly's calls to Loads.h API 2019-07-09 12:13:31 +00:00
test [test] Add wrap flags after D61934. 2019-06-17 19:17:07 +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.