llvm-project/polly
Tobias Grosser d5a7bfc51d ScopInfo: Do not return reference to member variable 'domain'.
Instead of returning a pointer to the domain, we return a new copy of it. This
is safer, as we do not give access to internal objects. It is also not
expensive, as isl will just increment a reference counter.

llvm-svn: 131010
2011-05-06 19:52:19 +00:00
..
autoconf Add initial version of Polly 2011-04-29 06:27:02 +00:00
cmake Add initial version of Polly 2011-04-29 06:27:02 +00:00
docs Add initial version of Polly 2011-04-29 06:27:02 +00:00
include ScopInfo: Do not return reference to member variable 'domain'. 2011-05-06 19:52:19 +00:00
lib ScopInfo: Do not return reference to member variable 'domain'. 2011-05-06 19:52:19 +00:00
test ScopDetection: Remember the functions generated by backend in a pointer set, so we 2011-05-06 02:38:20 +00:00
tools Add initial version of Polly 2011-04-29 06:27:02 +00:00
utils Add a converter from jscop to iscc input 2011-04-29 06:29:20 +00:00
www www: Finish first draft of the matmul example 2011-05-03 09:40:40 +00:00
CMakeLists.txt Add initial version of Polly 2011-04-29 06:27:02 +00:00
CREDITS.txt Add e-mail to credits file. 2011-04-29 07:54:20 +00:00
LICENSE.txt Add initial version of Polly 2011-04-29 06:27:02 +00:00
Makefile Add initial version of Polly 2011-04-29 06:27:02 +00:00
Makefile.common.in Add initial version of Polly 2011-04-29 06:27:02 +00:00
Makefile.config.in Add initial version of Polly 2011-04-29 06:27:02 +00:00
README Add initial version of Polly 2011-04-29 06:27:02 +00:00
configure Add initial version of Polly 2011-04-29 06:27:02 +00:00

README

Polly - Polyhedral optimizations for LLVM

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.