llvm-capstone/polly
Johannes Doerfert 90db75ed24 Runtime error check elimination
Hoist runtime checks in the loop nest if they guard an "error" like event.
  Such events are recognized as blocks with an unreachable terminator or a call
  to the ubsan function that deals with out of bound accesses. Other "error"
  events can be added easily.

  We will ignore these blocks when we detect/model/optmize and code generate SCoPs
  but we will make sure that they would not have been executed using the assumption
  framework.

llvm-svn: 247310
2015-09-10 17:51:27 +00:00
..
autoconf Generate gitversion.h in autoconf builds 2015-09-09 13:15:11 +00:00
cmake Unify FOLDER property of Polly targets 2015-07-21 12:40:01 +00:00
include/polly Runtime error check elimination 2015-09-10 17:51:27 +00:00
lib Runtime error check elimination 2015-09-10 17:51:27 +00:00
test Runtime error check elimination 2015-09-10 17:51:27 +00:00
tools Do not compile GPU library with sanitizers 2015-07-28 12:51:23 +00:00
utils Make arc unit work with ninja builds 2015-08-12 09:02:20 +00:00
www Do not use negative option name 2015-08-19 08:22:06 +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
.gitignore Add git patch files to .gitignore 2015-06-23 20:55:01 +00:00
CMakeLists.txt Do not use -fvisibility option with Visual C 2015-08-07 22:16:44 +00:00
configure Enable ISL's small integer optimization 2015-06-25 20:47:35 +00:00
CREDITS.txt Add myself to the credits 2014-08-10 03:37:29 +00:00
LICENSE.txt
Makefile
Makefile.common.in
Makefile.config.in Fix autotools build 2015-06-25 16:50:13 +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.