llvm-project/polly
Tobias Grosser 5857b701a3 GPGPU: Bail out gracefully in case of invalid IR
Instead of aborting, we now bail out gracefully in case the kernel IR we
generate is invalid. This can currently happen in case the SCoP stores
pointer values, which we model as arrays, as data values into other arrays. In
this case, the original pointer value is not available on the device and can
consequently not be stored. As detecting this ahead of time is not so easy, we
detect these situations after the invalid IR has been generated and bail out.

llvm-svn: 281193
2016-09-12 06:06:31 +00:00
..
cmake Add missing words to wanrning. 2016-08-25 13:29:26 +00:00
docs docs: Remove reference to PoCC 2016-05-17 19:44:16 +00:00
include/polly Add namespace specifier before nullptr_t 2016-09-09 12:31:38 +00:00
lib GPGPU: Bail out gracefully in case of invalid IR 2016-09-12 06:06:31 +00:00
test GPGPU: Bail out gracefully in case of invalid IR 2016-09-12 06:06:31 +00:00
tools GPURuntime: ensure compilation with C99 2016-09-11 07:32:50 +00:00
unittests Add -polly-flatten-schedule pass. 2016-09-08 15:02:36 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www Add forgotten image 2016-08-30 12:41:29 +00:00
.arcconfig Upgrade all the .arcconfigs to https. 2016-07-14 13:15:37 +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 Query llvm-config to get system libs required for linking. 2016-08-25 14:58:29 +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.