hanchenye-llvm-project/polly
Michael Kruse e0b34f366f Update to ISL 0.17.
This release includes sevaral improvments compared to the previous
version isl-0.16.1-145-g243bf7c (from the ISL 0.17 announcement):
- optionally combine SCCs incrementally in scheduler
- optionally maximize coincidence in scheduler
- optionally avoid loop coalescing in scheduler
- minor AST generator improvements
- improve support for expansions in schedule trees

llvm-svn: 268500
2016-05-04 14:41:36 +00:00
..
cmake Fix: Always honor LLVM_LIBDIR_SUFFIX. 2016-04-09 14:09:08 +00:00
docs doc: A source code with Polly does not use a separate module (by default) 2016-04-29 12:35:46 +00:00
include/polly [FIX] Unsigned comparisons change invalid domain 2016-04-29 10:44:41 +00:00
lib Update to ISL 0.17. 2016-05-04 14:41:36 +00:00
test Update to ISL 0.17. 2016-05-04 14:41:36 +00:00
tools Update copyright year to 2016. 2016-03-30 22:41:38 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www [WWW] Mark task as done and me as owner of some task 2016-05-02 11:21:30 +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
.gitignore Add git patch files to .gitignore 2015-06-23 20:55:01 +00:00
CMakeLists.txt cmake: Ensure tools/* is still formatted 2016-03-25 12:16:17 +00:00
CREDITS.txt
LICENSE.txt Update copyright year to 2016. 2016-03-30 22:41:38 +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.