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llvm-project/polly
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Tobias Grosser 604c981f40 Temporarily remove reduction support and interchange pass 
I am planning to eliminate the TempScopInfo pass. To simplify this I remove
some features that may later be added to the ScopInfo pass.

The interchange pass is currently strongly tested and furthermore ment to be
replaced by the general scheduling optimizer. Reductions itself can later
be added easily.

llvm-svn: 138219
 		2011-08-21 14:57:58 +00:00
..
autoconf Add initial version of Polly 2011-04-29 06:27:02 +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 Temporarily remove reduction support and interchange pass 2011-08-21 14:57:58 +00:00
lib Temporarily remove reduction support and interchange pass 2011-08-21 14:57:58 +00:00
test Temporarily remove reduction support and interchange pass 2011-08-21 14:57:58 +00:00
tools Add initial version of Polly 2011-04-29 06:27:02 +00:00
utils pollycc: Fix error message if PoCC/Pluto are not available 2011-07-06 18:04:59 +00:00
www www: Updating memaccess Documentation 2011-08-15 09:37:46 +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 Add initial version of Polly 2011-04-29 06:27:02 +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 Add initial version of Polly 2011-04-29 06:27:02 +00:00
configure Add initial version of Polly 2011-04-29 06:27:02 +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.