llvm-project/polly
Tobias Grosser 4fb9e51664 ScopInfo: Drop some debug statements
This debug output distracts from the -debug-only=polly-scops output. As it is
rather verbose and only really needed for debugging the domain construction
I drop this output. The domain construction is meanwhile stable enough to
not require regular debugging.

llvm-svn: 262117
2016-02-27 06:59:30 +00:00
..
cmake Compile ISL into its own library 2015-09-24 11:30:22 +00:00
docs Support accesses with differently sized types to the same array 2016-02-04 13:18:42 +00:00
include/polly LoopGenerators: Expose some parts of the parallel loop generator 2016-02-27 06:24:58 +00:00
lib ScopInfo: Drop some debug statements 2016-02-27 06:59:30 +00:00
test Update the fine-grain dependences analysis test case. 2016-02-27 01:50:01 +00:00
tools Remove autotools build system 2016-01-28 12:00:33 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www www: Fix typo 2016-02-25 15:21:02 +00:00
.arcconfig Adjusted arc linter config for modern version of arcanist 2015-08-12 09:01:16 +00:00
.arclint Adjusted arc linter config for modern version of arcanist 2015-08-12 09:01:16 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
.gitignore Add git patch files to .gitignore 2015-06-23 20:55:01 +00:00
CMakeLists.txt Add basic doxygen infrastructure for Polly 2016-02-04 07:16:36 +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
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.