llvm-project

History

Hongbin Zheng dbdebe28de Refactor: Move 'isParallelFor' from codegen backend to Dependences analysis, so other passes can also use it. llvm-svn: 130752		2011-05-03 13:46:58 +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	Refactor: Move 'isParallelFor' from codegen backend to Dependences analysis, so other passes can also use it.	2011-05-03 13:46:58 +00:00
lib	Refactor: Move 'isParallelFor' from codegen backend to Dependences analysis, so other passes can also use it.	2011-05-03 13:46:58 +00:00
test	Partial support test polly for out of tree build.	2011-04-29 07:34:54 +00:00
tools	Add initial version of Polly	2011-04-29 06:27:02 +00:00
utils	Add a converter from jscop to iscc input	2011-04-29 06:29:20 +00:00
www	www: Finish first draft of the matmul example	2011-05-03 09:40:40 +00:00
CMakeLists.txt	Add initial version of Polly	2011-04-29 06:27:02 +00:00
CREDITS.txt	Add e-mail to credits file.	2011-04-29 07:54:20 +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	Add initial version of Polly	2011-04-29 06:27:02 +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.