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Tobias Grosser 0dd463facf Support for generating vectors for loads with -1 stride 
This patch enables vectorization of loops containing backward array
traversal (array stride is -1).

Contributed-by: Chris Jenneisch <chrisj@codeaurora.org>
llvm-svn: 204257
 		2014-03-19 19:27:24 +00:00
..
autoconf GMP is only required for CLooG 2014-02-22 02:15:39 +00:00
cmake record in POLLY_LINK_LIBS all the libs needed for polly 2014-03-13 20:24:48 +00:00
docs
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include Support for generating vectors for loads with -1 stride 2014-03-19 19:27:24 +00:00
lib Support for generating vectors for loads with -1 stride 2014-03-19 19:27:24 +00:00
test Support for generating vectors for loads with -1 stride 2014-03-19 19:27:24 +00:00
tools Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
utils Update CLooG and some test cases 2014-03-10 17:31:22 +00:00
www www: Fix link to Pluto 2014-03-10 12:28:20 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
CMakeLists.txt record in POLLY_LINK_LIBS all the libs needed for polly 2014-03-13 20:24:48 +00:00
CREDITS.txt
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LICENSE.txt Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
Makefile
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Makefile.common.in 'chmod -x' on files that do not need the executable bits 2012-12-29 15:09:03 +00:00
Makefile.config.in autoconf: Add PLUTO_FOUND flag 2014-03-18 18:50:58 +00:00
README Trivial change to the README, mainly to test commit access. 2012-10-09 04:59:42 +00:00
configure GMP is only required for CLooG 2014-02-22 02:15:39 +00:00

README 

Polly - Polyhedral optimizations for LLVM
-----------------------------------------
http://polly.llvm.org/

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.