ca7f5bb767
We isolate full tiles from partial tiles to be able to, for example, vectorize loops with parametric lower and/or upper bounds. If we use -polly-vectorizer=stripmine, we can see execution-time improvements: correlation from 1m7361s to 0m5720s (-67.05 %), covariance from 1m5561s to 0m5680s (-63.50 %), ary3 from 2m3201s to 1m2361s (-46.72 %), CrystalMk from 8m5565s to 7m4285s (-13.18 %). The current full/partial tile separation increases compile-time more than necessary. As a result, we see in compile time regressions, for example, for 3mm from 0m6320s to 0m9881s (56.34%). Some of this compile time increase is expected as we generate more IR and consequently more time is spent in the LLVM backends. However, a first investiagation has shown that a larger portion of compile time is unnecessarily spent inside Polly's parallelism detection and could be eliminated by propagating existing knowledge about vector loop parallelism. Before enabling -polly-vectorizer=stripmine by default, it is necessary to address this compile-time issue. Contributed-by: Roman Gareev <gareevroman@gmail.com> Reviewers: jdoerfert, grosser Subscribers: grosser, #polly Differential Revision: http://reviews.llvm.org/D13779 llvm-svn: 250809 |
||
|---|---|---|
| .. | ||
| autoconf | ||
| cmake | ||
| include/polly | ||
| lib | ||
| test | ||
| tools | ||
| utils | ||
| www | ||
| .arcconfig | ||
| .arclint | ||
| .gitattributes | ||
| .gitignore | ||
| 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.