llvm-capstone/polly
Eli Friedman fd229caa01 [polly] Fix SCEVLoopAddRecRewriter to avoid invalid AddRecs.
When we're remapping an AddRec, the AddRec constructed by a partial
rewrite might not make sense.  This triggers an assertion complaining
it's not loop-invariant.

Instead of constructing the partially rewritten AddRec, just skip
straight to calling evaluateAtIteration.

Testcase was automatically reduced using llvm-reduce, so it's a little
messy, but hopefully makes sense.

Differential Revision: https://reviews.llvm.org/D102959
2021-06-01 09:51:05 -07:00
..
cmake [Windows][Polly] Disable LLVMPolly module for all compilers on Windows 2020-09-15 09:12:38 +03:00
docs Revert "[NFC] remove explicit default value for strboolattr attribute in tests" 2021-05-24 19:43:40 +02:00
include/polly [Polly] Add support for -polly-dump-before(-file) with the NPM. 2021-05-17 20:58:37 -05:00
lib [Polly] Avoid compiler warning. NFC. 2021-05-22 00:21:20 -05:00
test [polly] Fix SCEVLoopAddRecRewriter to avoid invalid AddRecs. 2021-06-01 09:51:05 -07:00
tools Fix typos throughout the license files that somehow I and my reviewers 2019-01-21 09:52:34 +00:00
unittests [Polly] Regenerate isl-noexceptions.h. 2021-02-14 19:17:54 -06:00
utils Harmonize Python shebang 2020-07-16 21:53:45 +02:00
www [Branch-Rename] Fix some links 2021-02-01 16:43:21 +05:30
.arclint
.gitattributes
.gitignore
CMakeLists.txt Remove .svn from exclude list as we moved to git 2020-10-21 16:09:21 +02:00
CREDITS.txt
LICENSE.TXT Rename top-level LICENSE.txt files to LICENSE.TXT 2021-03-10 21:26:24 -08:00
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.