Speeding up Iterative Polyhedral Schedule Optimization with Surrogate Performance Models

Iterative program optimization is known to be able to adapt more easily to particular programs and target hardware than model-based approaches. An approach is to generate random program transformations and evaluate their profitability by applying them and benchmarking the transformed program on the target hardware. This procedure’s large computational effort impairs its practicality tremendously, though. To address this limitation, we pursue the guidance of a genetic algorithm for program optimization via feedback from surrogate performance models. We train the models on program transformations that were evaluated during previous iterative optimizations. Our representation of programs and program transformations refers to the polyhedron model. The representation is particularly meaningful for an optimization of loop programs that profit a from coarse-grained parallelization for execution on modern multicore-CPUs. Our evaluation reveals that surrogate performance models can be used to speed up the optimization of loop programs. We demonstrate that we can reduce the benchmarking effort required for an iterative optimization and degrade the resulting speedups by an average of 15%.