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llvm-project/polly
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Tobias Grosser 4cb5461dae CodeGen: Generate scalar code if vector instructions cannot be generated 
This fixes two crashes that appeared in case of:
  - A load of a non vectorizable type (e.g. float**)
  - An instruction that is not vectorizable (e.g. call)

llvm-svn: 154586
 		2012-04-12 10:46:55 +00:00
..
autoconf configure: Add gmp_inc when checking for CLooG 2011-10-04 06:55:03 +00:00
cmake Add initial version of Polly 2011-04-29 06:27:02 +00:00
docs Add initial version of Polly 2011-04-29 06:27:02 +00:00
include CodeGen: Extract the LLVM-IR generaction of scalar and OpenMP loops. 2012-03-23 10:35:18 +00:00
lib CodeGen: Generate scalar code if vector instructions cannot be generated 2012-04-12 10:46:55 +00:00
test CodeGen: Generate scalar code if vector instructions cannot be generated 2012-04-12 10:46:55 +00:00
tools Add initial version of Polly 2011-04-29 06:27:02 +00:00
utils Update isl 2012-02-20 08:41:44 +00:00
www www: Fix typo, replace "LD_LBIRARY_PATH" by "LD_LIBRARY_PATH" in get_started. 2012-04-03 09:15:52 +00:00
CMakeLists.txt Out of tree build support: Set TARGET_TRIPLE from the result of "llvm-config --host-target" 2012-03-27 07:56:07 +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 Buildsystem: Add -no-rtti 2011-06-30 19:50:04 +00:00
README Remove some empty lines 2011-10-04 06:56:36 +00:00
configure configure: Add gmp_inc when checking for CLooG 2011-10-04 06:55:03 +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.