maxjhandsome
/
llvm-project
forked from OSchip/llvm-project
1
0
Fork 0
Code Issues Pull Requests Packages Releases Wiki Activity
llvm-project/polly
History
Tobias Grosser 0b6c613a10 CodeGen: Maintain a valid CFG during code generation 
Before this change we built the CFG such that it was only valid after code was
fully generated. During code generation itself, it was often incomplete. After
this change always maintain a valid CFG. This will later allow us to use the
SCEVExpander during code generation. This is the first step to get rid of the
independent blocks pass.

llvm-svn: 150339
 		2012-02-12 12:09:53 +00:00
..
autoconf configure: Add gmp_inc when checking for CLooG 2011-10-04 06:55:03 +00:00
cmake Add initial version of Polly 2011-04-29 06:27:02 +00:00
docs Add initial version of Polly 2011-04-29 06:27:02 +00:00
include Add a sceleton for a polyhedral dead code elimination. 2012-01-31 14:00:27 +00:00
lib CodeGen: Maintain a valid CFG during code generation 2012-02-12 12:09:53 +00:00
test CodeGen: Always name merge block 2012-02-12 12:09:46 +00:00
tools Add initial version of Polly 2011-04-29 06:27:02 +00:00
utils Use isl version: 3c66541593a6bf3b5a3d35d31567abe6c9e5a04b 2012-01-30 19:38:40 +00:00
www www: More typos 2012-02-01 00:08:10 +00:00
CMakeLists.txt Buildsystem: Add -no-rtti 2011-06-30 19:50:04 +00:00
CREDITS.txt (Test commit for polly) 2011-07-16 13:30:03 +00:00
LICENSE.txt Happy new year 2012! 2012-01-01 08:16:56 +00:00
Makefile Add initial version of Polly 2011-04-29 06:27:02 +00:00
Makefile.common.in Add initial version of Polly 2011-04-29 06:27:02 +00:00
Makefile.config.in Buildsystem: Add -no-rtti 2011-06-30 19:50:04 +00:00
README Remove some empty lines 2011-10-04 06:56:36 +00:00
configure configure: Add gmp_inc when checking for CLooG 2011-10-04 06:55:03 +00:00

README 

Polly - Polyhedral optimizations for LLVM

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.