llvm-project/polly
Mandeep Singh Grang 8c5314479e Fix spacing around variable initializations and for-loops. NFC.
Reviewers: grosser, _jdoerfert, zinob

Projects: #polly

Differential Revision: https://reviews.llvm.org/D23285

llvm-svn: 278143
2016-08-09 17:49:24 +00:00
..
cmake Respect LLVM_INSTALL_TOOLCHAIN_ONLY. 2016-06-21 18:14:01 +00:00
docs docs: Remove reference to PoCC 2016-05-17 19:44:16 +00:00
include/polly [IslNodeBuilder] Move run-time check generation to NodeBuilder [NFC] 2016-08-08 15:41:52 +00:00
lib Fix spacing around variable initializations and for-loops. NFC. 2016-08-09 17:49:24 +00:00
test [GPGPU] Support PHI nodes used in GPU kernel 2016-08-09 15:35:06 +00:00
tools GPGPU: Cache PTX kernels 2016-08-04 09:15:58 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www Fix spacing around variable initializations and for-loops. NFC. 2016-08-09 17:49:24 +00:00
.arcconfig Upgrade all the .arcconfigs to https. 2016-07-14 13:15:37 +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 GPGPU: Shorten ppcg include paths to avoid conflict with cuda.h 2016-07-15 07:50:36 +00:00
CREDITS.txt Add myself to the credits 2014-08-10 03:37:29 +00:00
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.