llvm-project

History

Tobias Grosser 903c242662 Update libGPURuntime to be dual licensed under MIT and UIUC license. Contributed by: Yabin Hu <yabin.hwu@gmail.com> llvm-svn: 159815		2012-07-06 10:40:15 +00:00
..
autoconf	Detect the cuda library available.	2012-06-06 12:16:10 +00:00
cmake	Replace CUDA data types with Polly's GPGPU data types.	2012-07-04 21:45:03 +00:00
docs	Add initial version of Polly	2011-04-29 06:27:02 +00:00
include	Add an Instruction member to MemoryAccess Class.	2012-07-06 06:47:03 +00:00
lib	Add an Instruction member to MemoryAccess Class.	2012-07-06 06:47:03 +00:00
test	Add some tests for the independent blocks pass.	2012-06-11 10:25:12 +00:00
tools	Update libGPURuntime to be dual licensed under MIT and UIUC license.	2012-07-06 10:40:15 +00:00
utils	codegen.intrinsic: Update testcase to work with NVPTX backend	2012-07-03 08:18:34 +00:00
www	www: Add GPGPU Code Generation Documentation.	2012-05-30 13:54:02 +00:00
CMakeLists.txt	Replace CUDA data types with Polly's GPGPU data types.	2012-07-04 21:45:03 +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	Revert "Fix a bug introduced by r153739: We are not able to provide the correct"	2012-04-11 07:43:13 +00:00
Makefile.common.in	Add initial version of Polly	2011-04-29 06:27:02 +00:00
Makefile.config.in	Detect the cuda library available.	2012-06-06 12:16:10 +00:00
README	Remove some empty lines	2011-10-04 06:56:36 +00:00
configure	Detect the cuda library available.	2012-06-06 12:16:10 +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.