llvm-project/polly
Tobias Grosser 3f29619614 Drop all constant scheduling dimensions
Schedule dimensions that have the same constant value accross all statements do
not carry any information, but due to the increased dimensionality of the
schedule cost compile time. To not pay this cost, we remove constant dimensions
if possible.

llvm-svn: 225067
2015-01-01 23:01:11 +00:00
..
autoconf Drop Cloog support 2014-12-02 19:26:58 +00:00
cmake Drop Cloog support 2014-12-02 19:26:58 +00:00
include Drop all constant scheduling dimensions 2015-01-01 23:01:11 +00:00
lib Drop all constant scheduling dimensions 2015-01-01 23:01:11 +00:00
test Drop all constant scheduling dimensions 2015-01-01 23:01:11 +00:00
tools Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
utils Update to the latest version of isl 2014-12-07 16:04:29 +00:00
www www-todo: No need to directly integrate with the basic block vectorizer 2014-12-07 15:57:29 +00:00
.arcconfig Added arcanist (arc) unit test support 2014-09-08 19:30:09 +00:00
.arclint Added arcanist linters and cleaned errors and warnings 2014-08-18 00:40:13 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
.gitignore Add test/lit.site.cfg to .gitignore 2014-09-07 15:03:30 +00:00
CMakeLists.txt Drop Cloog support 2014-12-02 19:26:58 +00:00
CREDITS.txt Add myself to the credits 2014-08-10 03:37:29 +00:00
LICENSE.txt Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
Makefile
Makefile.common.in 'chmod -x' on files that do not need the executable bits 2012-12-29 15:09:03 +00:00
Makefile.config.in Drop Cloog support 2014-12-02 19:26:58 +00:00
README
configure Drop Cloog support 2014-12-02 19:26:58 +00:00

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.