llvm-project/polly
Arthur Eubanks cabe1b1124 [polly][NewPM][test] Fix polly tests under -enable-new-pm
In preparation for turning on opt's -enable-new-pm by default, this pins
uses of passes via the legacy "opt -passname" with pass names beginning
with "polly-" and "polyhedral-info" to the legacy PM. Many of these
tests use -analyze, which isn't supported in the new PM.

(This doesn't affect uses of "opt -passes=passname").

rL240766 accidentally removed `-polly-prepare` in
phi_not_grouped_at_top.ll, and it also doesn't use the output of
-analyze.

Reviewed By: Meinersbur

Differential Revision: https://reviews.llvm.org/D94266
2021-01-19 12:38:58 -08:00
..
cmake [Windows][Polly] Disable LLVMPolly module for all compilers on Windows 2020-09-15 09:12:38 +03:00
docs Bump the trunk major version to 12 2020-07-15 12:05:05 +02:00
include/polly [Polly] Consider InvalidContext to determine partial READ. 2020-12-10 22:25:19 -06:00
lib [Polly] Update isl to isl-0.23-61-g24e8cd12. 2021-01-19 12:01:31 -06:00
test [polly][NewPM][test] Fix polly tests under -enable-new-pm 2021-01-19 12:38:58 -08:00
tools Fix typos throughout the license files that somehow I and my reviewers 2019-01-21 09:52:34 +00:00
unittests [Polly] Support linking ScopPassManager against LLVM dylib 2020-08-07 06:46:35 +02:00
utils Harmonize Python shebang 2020-07-16 21:53:45 +02:00
www [BasicAA] Replace -basicaa with -basic-aa in polly 2020-06-30 15:50:17 -07:00
.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 Fix typos throughout the license files that somehow I and my reviewers 2019-01-21 09:52:34 +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.