forked from OSchip/llvm-project
ecff11dcfb
To reduce compile time and to allow more and better quality SCoPs in the long run we introduced scalar dependences and PHI-modeling. This patch will now allow us to generate code if one or both of those options are set. While the principle of demoting scalars as well as PHIs to memory in order to communicate their value stays the same, this allows to delay the demotion till the very end (the actual code generation). Consequently: - We __almost__ do not modify the code if we do not generate code for an optimized SCoP in the end. Thus, the early exit as well as the unprofitable option will now actually preven us from introducing regressions in case we will probably not get better code. - Polly can be used as a "pure" analyzer tool as long as the code generator is set to none. - The original SCoP is almost not touched when the optimized version is placed next to it. Runtime regressions if the runtime checks chooses the original are not to be expected and later optimizations do not need to revert the demotion for that part. - We will generate direct accesses to the demoted values, thus there are no "trivial GEPs" that select the first element of a scalar we demoted and treated as an array. Differential Revision: http://reviews.llvm.org/D7513 llvm-svn: 238070 |
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test | ||
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www | ||
.arcconfig | ||
.arclint | ||
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CMakeLists.txt | ||
CREDITS.txt | ||
LICENSE.txt | ||
Makefile | ||
Makefile.common.in | ||
Makefile.config.in | ||
README | ||
configure |
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.