llvm-project/polly
Tobias Grosser 3cbe5cfff3 Remove FinalRead
The FinalRead statement represented a virtual read that is executed after the
SCoP. It was used when we verified the correctness of a schedule by checking if
it yields the same FLOW dependences as the original code. This is only works, if
we have a final read that reads all memory at the end of the SCoP.
We now switched to just checking if a schedule does not introduce negative
dependences and also consider WAW WAR dependences. This restricts the schedules
a little bit more, but we do not have any optimizer that would calculate a more
complex schedule. Hence, for now final reads are obsolete.

llvm-svn: 152319
2012-03-08 15:21:51 +00:00
..
autoconf configure: Add gmp_inc when checking for CLooG 2011-10-04 06:55:03 +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 Remove FinalRead 2012-03-08 15:21:51 +00:00
lib Remove FinalRead 2012-03-08 15:21:51 +00:00
test Remove FinalRead 2012-03-08 15:21:51 +00:00
tools Add initial version of Polly 2011-04-29 06:27:02 +00:00
utils Update isl 2012-02-20 08:41:44 +00:00
www www: Really fix it 2012-03-08 12:02:59 +00:00
CMakeLists.txt Buildsystem: Add -no-rtti 2011-06-30 19:50:04 +00:00
CREDITS.txt (Test commit for polly) 2011-07-16 13:30:03 +00:00
LICENSE.txt Happy new year 2012! 2012-01-01 08:16:56 +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 Buildsystem: Add -no-rtti 2011-06-30 19:50:04 +00:00
README Remove some empty lines 2011-10-04 06:56:36 +00:00
configure configure: Add gmp_inc when checking for CLooG 2011-10-04 06:55:03 +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.