llvm-project

History

Tobias Grosser 1df5289782 autoconf: Only define GPGPU_CODEGEN, if that feature is requested Before we defined GPGPU_CODEGEN to '0', which does not disable the relevant code as we just check if that value is defined at all. We now follow the cmake approach and only define GPGPU_CODEGEN, if the feature should be enabled. Reported by: Sebastian Pop <spop@codeaurora.org> llvm-svn: 162275		2012-08-21 12:29:10 +00:00
..
autoconf	autoconf: Only define GPGPU_CODEGEN, if that feature is requested	2012-08-21 12:29:10 +00:00
cmake	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +00:00
docs	…
include	Add preliminary implementation for GPGPU code generation.	2012-08-03 12:50:07 +00:00
lib	Add preliminary implementation for GPGPU code generation.	2012-08-03 12:50:07 +00:00
test	Add preliminary implementation for GPGPU code generation.	2012-08-03 12:50:07 +00:00
tools	Update libGPURuntime to be dual licensed under MIT and UIUC license.	2012-07-06 10:40:15 +00:00
utils	Update llvm.codegen() patch for CodeGen.cpp changes in r159694.	2012-08-02 08:16:40 +00:00
www	Remove executable bits from html files	2012-08-15 05:50:24 +00:00
CMakeLists.txt	Add preliminary implementation for GPGPU code generation.	2012-08-03 12:50:07 +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	…
Makefile.config.in	Add support for libpluto as the scheduling optimizer.	2012-08-02 07:47:26 +00:00
README	Remove some empty lines	2011-10-04 06:56:36 +00:00
configure	autoconf: Only define GPGPU_CODEGEN, if that feature is requested	2012-08-21 12:29:10 +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.