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llvm-project/polly
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Tobias Grosser 8a5bc6edca Add a new isl based code generation 
This pass implements a new code generator that uses the code generation
algorithm included in isl.

For the moment the new code generation is limited to sequential code.

llvm-svn: 165037
 		2012-10-02 19:50:43 +00:00
..
autoconf Detect the isl code generation feature correctly 2012-10-02 19:50:22 +00:00
cmake Detect the isl code generation feature correctly 2012-10-02 19:50:22 +00:00
docs Add initial version of Polly 2011-04-29 06:27:02 +00:00
include Add a new isl based code generation 2012-10-02 19:50:43 +00:00
lib Add a new isl based code generation 2012-10-02 19:50:43 +00:00
test Add test cases for multi-dimensional variable lengths arrays 2012-09-11 14:03:19 +00:00
tools Update libGPURuntime to be dual licensed under MIT and UIUC license. 2012-07-06 10:40:15 +00:00
utils Update isl to get the new code generation 2012-10-02 19:50:30 +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 Detect the isl code generation feature correctly 2012-10-02 19:50:22 +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.