forked from OSchip/llvm-project
f1ac0fcf56
Summary: With small integer optimization (short: sio) enabled, ISL uses 32 bit integers for its arithmetic and only falls back to a big integer library (in the case of Polly: IMath) if an operation's result is too large. This gives a massive performance boost for most application using ISL. For instance, experiments with ppcg (polyhedral source-to-source compiler) show speed-ups of 5.8 (compared to plain IMath), respectively 2.7 (compared to GMP). In Polly, a smaller fraction of the total compile time is taken by ISL, but the speed-ups are still very significant. The buildbots measure compilation speed-up up to 1.8 (oourafft, floyd-warshall, symm). All Polybench benchmarks compile in at least 9% less time, and about 20% less on average. Detailed Polybench compile time results (median of 10): correlation -25.51% covariance -24.82% 2mm -26.64% 3mm -28.69% atax -13.70% bicg -10.78% cholesky -40.67% doitgen -11.60% gemm -11.54% gemver -10.63% gesummv -11.54% mvt -9.43% symm -41.25% syr2k -14.71% syrk -14.52% trisolv -17.65% trmm -9.78% durbin -19.32% dynprog -9.09% gramschmidt -15.38% lu -21.77% floyd-warshall -42.71% reg_detect -41.17% adi -36.69% fdtd-2d -32.61% fdtd-apml -21.90% jacobi-1d-imper -9.41% jacobi-2d-imper -27.65% seidel-2d -31.00% Reviewers: grosser Reviewed By: grosser Subscribers: Meinersbur, llvm-commits, pollydev Projects: #polly Differential Revision: http://reviews.llvm.org/D10506 llvm-svn: 240689 |
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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.