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llvm-project/polly
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Johannes Doerfert c3958b214c Added option for n-dimensional rectangular tiling 
+ CL-option --polly-tile-sizes=<int,...,int>
  The i'th value is used as a tile size for dimension i, if
  there is no i'th value, the value of --polly-default-tile-size is
  used

+ CL-option --polly-default-tile-size=int
  Used if no tile size is given for a dimension i

+ 3 Simple testcases

llvm-svn: 209753
 		2014-05-28 17:21:02 +00:00
..
autoconf Remove OpenScop 2014-04-11 09:47:45 +00:00
cmake Remove OpenScop 2014-04-11 09:47:45 +00:00
docs
…
include pass element size to delinearization 2014-05-27 22:42:09 +00:00
lib Added option for n-dimensional rectangular tiling 2014-05-28 17:21:02 +00:00
test Added option for n-dimensional rectangular tiling 2014-05-28 17:21:02 +00:00
tools Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +00:00
utils Update isl to fix memory bugs 2014-04-13 16:37:18 +00:00
www www: Reference phone call notes 2014-04-23 18:09:24 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
CMakeLists.txt Reorder cmake include folders (polly source first) 2014-05-28 16:54:42 +00:00
CREDITS.txt (Test commit for polly) 2011-07-16 13:30:03 +00:00
LICENSE.txt Update the copyright credits -- Happy new year 2014! 2014-01-01 08:27:31 +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 'chmod -x' on files that do not need the executable bits 2012-12-29 15:09:03 +00:00
Makefile.config.in Remove OpenScop 2014-04-11 09:47:45 +00:00
README Trivial change to the README, mainly to test commit access. 2012-10-09 04:59:42 +00:00
configure Remove OpenScop 2014-04-11 09:47:45 +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.