forked from OSchip/llvm-project
f67433abd9
Assumptions can either be added for a given basic block, in which case the set describing the assumptions is expected to match the dimensions of its domain. In case no basic block is provided a parameter-only set is expected to describe the assumption. The piecewise expressions that are generated by the SCEVAffinator sometimes have a zero-dimensional domain (e.g., [p] -> { [] : p <= -129 or p >= 128 }), which looks similar to a parameter-only domain, but is still a set domain. This change adds an assert that checks that we always pass parameter domains to addAssumptions if BB is empty to make mismatches here fail early. We also change visitTruncExpr to always convert to parameter sets, if BB is null. This change resolves http://llvm.org/PR30941 Another alternative to this change would have been to inspect all code to make sure we directly generate in the SCEV affinator parameter sets in case of empty domains. However, this would likely complicate the code which combines parameter and non-parameter domains when constructing a statement domain. We might still consider doing this at some point, but as this likely requires several non-local changes this should probably be done as a separate refactoring. Reported-by: Eli Friedman <efriedma@codeaurora.org> llvm-svn: 286444 |
||
---|---|---|
.. | ||
cmake | ||
docs | ||
include/polly | ||
lib | ||
test | ||
tools | ||
unittests | ||
utils | ||
www | ||
.arcconfig | ||
.arclint | ||
.gitattributes | ||
.gitignore | ||
CMakeLists.txt | ||
CREDITS.txt | ||
LICENSE.txt | ||
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.