llvm-project/polly
Michael Kruse 6fa65f8a98 [Polly][MatMul] Abandon dependence analysis.
The copy statements inserted by the matrix-multiplication optimization
introduce new dependencies between the copy statements and other
statements. As a result, the DependenceInfo must be recomputed.

Not recomputing them caused IslAstInfo to deduce that some loops are
parallel but cause race conditions when accessing the packed arrays.
As a result, matrix-matrix multiplication currently cannot be
parallelized.

Also see discussion at https://reviews.llvm.org/D125202
2022-06-29 17:20:05 -05:00
..
cmake [polly][cmake] Use `GNUInstallDirs` to support custom installation dirs 2022-01-18 20:33:42 +00:00
docs Bump the trunk major version to 15 2022-02-01 23:54:52 -08:00
include/polly [Polly][MatMul] Abandon dependence analysis. 2022-06-29 17:20:05 -05:00
lib [Polly][MatMul] Abandon dependence analysis. 2022-06-29 17:20:05 -05:00
test [Polly][MatMul] Abandon dependence analysis. 2022-06-29 17:20:05 -05:00
tools
unittests [polly][unittests] Link DeLICMTests with libLLVMCore 2022-01-28 21:58:40 +01:00
utils Harmonize Python shebang 2020-07-16 21:53:45 +02:00
www [Polly] Clean up Polly's getting started docs. 2021-10-14 12:26:57 -05:00
.arclint
.gitattributes
.gitignore
CMakeLists.txt [cmake] Make include(GNUInstallDirs) always below project(..) 2022-01-20 18:59:17 +00:00
CREDITS.txt
LICENSE.TXT Rename top-level LICENSE.txt files to LICENSE.TXT 2021-03-10 21:26:24 -08:00
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.