llvm-project/polly
Johannes Doerfert 8ab2803b63 [FIX] Propagate execution domain of invariant loads
If the base pointer of an invariant load is is loaded conditionally, that
  condition needs to hold for the invariant load too. The structure of the
  program will imply this for domain constraints but not for imprecisions in
  the modeling. To this end we will propagate the execution context of base
  pointers during code generation and thus ensure the derived pointer does
  not access an invalid base pointer.

llvm-svn: 267707
2016-04-27 12:49:11 +00:00
..
cmake Fix: Always honor LLVM_LIBDIR_SUFFIX. 2016-04-09 14:09:08 +00:00
docs docs: Fix section header committed in r264575 2016-03-28 17:00:14 +00:00
include/polly [FIX] Propagate execution domain of invariant loads 2016-04-27 12:49:11 +00:00
lib [FIX] Propagate execution domain of invariant loads 2016-04-27 12:49:11 +00:00
test [FIX] Propagate execution domain of invariant loads 2016-04-27 12:49:11 +00:00
tools Update copyright year to 2016. 2016-03-30 22:41:38 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www [WWW] Update passes 2016-04-05 16:15:44 +00:00
.arcconfig Adjusted arc linter config for modern version of arcanist 2015-08-12 09:01:16 +00:00
.arclint Adjusted arc linter config for modern version of arcanist 2015-08-12 09:01:16 +00:00
.gitattributes gitattributes: .png and .txt are no text files 2013-07-28 09:05:20 +00:00
.gitignore Add git patch files to .gitignore 2015-06-23 20:55:01 +00:00
CMakeLists.txt cmake: Ensure tools/* is still formatted 2016-03-25 12:16:17 +00:00
CREDITS.txt Add myself to the credits 2014-08-10 03:37:29 +00:00
LICENSE.txt Update copyright year to 2016. 2016-03-30 22:41:38 +00:00
README

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.