forked from OSchip/llvm-project
8d5b257d02
Summary:
When trying to expand memory accesses, the current version of Polly uses statement Level dependences. The actual implementation is not working in case of multiple dependences per statement. For example in the following source code :
```
void mse(double A[Ni], double B[Nj], double C[Nj], double D[Nj]) {
int i,j;
for (j = 0; j < Ni; j++) {
for (int i = 0; i<Nj; i++)
S: B[i] = i;
for (int i = 0; i<Nj; i++)
T: D[i] = i;
U: A[j] = B[j];
C[j] = D[j];
}
}
```
The statement U has two dependences with S and T. The current version of polly fails during expansion.
This patch aims to fix this bug. For that, we use Reference Level dependences to be able to filter dependences according to statement and memory ref. The principle of expansion remains the same as before.
We also noticed that we need to bail out if load come after store (at the same position) in same statement. So a check was added to isExpandable.
Contributed by: Nicholas Bonfante <nicolas.bonfante@insa-lyon.fr>
Reviewers: Meinersbur, simbuerg, bollu
Reviewed By: Meinersbur, simbuerg
Subscribers: pollydev, llvm-commits
Differential Revision: https://reviews.llvm.org/D36791
llvm-svn: 311165
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| .. | ||
| 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.