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llvm-project/polly
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Tobias Grosser 882a283946 Dependences: Print dependences in -analyze output 
The dependency printing was accidentally removed in during a previous
restructuring.

llvm-svn: 162662
 		2012-08-27 08:44:15 +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 Add initial version of Polly 2011-04-29 06:27:02 +00:00
include Add preliminary implementation for GPGPU code generation. 2012-08-03 12:50:07 +00:00
lib Dependences: Print dependences in -analyze output 2012-08-27 08:44:15 +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 Add initial version of Polly 2011-04-29 06:27:02 +00:00
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.