llvm-project/polly
Tobias Grosser 25184fe925 Allow cast instructions within scops
Cast instruction do not have side effects and can consequently be part of a
scop. We special cased them earlier, as they may be problematic within array
subscripts or loop bounds. However, the scalar evolution validator already
checks for them such that there is no need to also check the instructions within
the basic blocks.  Checking them is actually overly conservative as the precence
of casts may invalidate a scop, even though scalar evolution is not influenced
by it.

llvm-svn: 160261
2012-07-16 10:57:32 +00:00
..
autoconf Detect the cuda library available. 2012-06-06 12:16:10 +00:00
cmake Replace CUDA data types with Polly's GPGPU data types. 2012-07-04 21:45:03 +00:00
docs
include Revert "Add preliminary implementation for GPGPU code generation." 2012-07-13 07:44:56 +00:00
lib Allow cast instructions within scops 2012-07-16 10:57:32 +00:00
test Revert "Add preliminary implementation for GPGPU code generation." 2012-07-13 07:44:56 +00:00
tools Update libGPURuntime to be dual licensed under MIT and UIUC license. 2012-07-06 10:40:15 +00:00
utils codegen.intrinsic: Update testcase to work with NVPTX backend 2012-07-03 08:18:34 +00:00
www www: Add GPGPU Code Generation Documentation. 2012-05-30 13:54:02 +00:00
CMakeLists.txt Replace CUDA data types with Polly's GPGPU data types. 2012-07-04 21:45:03 +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 Revert "Fix a bug introduced by r153739: We are not able to provide the correct" 2012-04-11 07:43:13 +00:00
Makefile.common.in
Makefile.config.in Detect the cuda library available. 2012-06-06 12:16:10 +00:00
README Remove some empty lines 2011-10-04 06:56:36 +00:00
configure Detect the cuda library available. 2012-06-06 12:16:10 +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.