be5299af0b
Typically processor architectures do not include an L3 cache, which means that Nc, the parameter of the micro-kernel, is, for all practical purposes, redundant ([1]). However, its small values can cause the redundant packing of the same elements of the matrix A, the first operand of the matrix multiplication. At the same time, big values of the parameter Nc can cause segmentation faults in case the available stack is exceeded. This patch adds an option to specify the parameter Nc as a multiple of the parameter of the micro-kernel Nr. In case of Intel Core i7-3820 SandyBridge and the following options, clang -O3 gemm.c -I utilities/ utilities/polybench.c -DPOLYBENCH_TIME -march=native -mllvm -polly -mllvm -polly-pattern-matching-based-opts=true -DPOLYBENCH_USE_SCALAR_LB -mllvm -polly-target-cache-level-associativity=8,8 -mllvm -polly-target-cache-level-sizes=32768,262144 -mllvm -polly-target-latency-vector-fma=8 it helps to improve the performance from 11.303 GFlops/sec (39,247% of theoretical peak) to 17.896 GFlops/sec (62,14% of theoretical peak). Refs.: [1] - http://www.cs.utexas.edu/users/flame/pubs/TOMS-BLIS-Analytical.pdf Reviewed-by: Tobias Grosser <tobias@grosser.es> Differential Revision: https://reviews.llvm.org/D28019 llvm-svn: 290256 |
||
|---|---|---|
| .. | ||
| 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.