Cost-Model Driven Integration of Restructuring Optimizations

Loop transformation and array restructuring are important compiler optimizations that improve memory locality in complementary ways. Although previous researchers have proposed integrating the two techniques, there exists no analytical framework for determining how best to combine them for a given program. In this paper, we propose a cost model for choosing between all combinations of loop and array restructuring options for a given loop nest. Since the choice of which optimization to apply, alone or in combination, is highly application and/or input dependent, such a cost model is crucial if integrated restructuring is to be automated by an optimizing compiler. Our cost model considers two potential forms of array restructuring: conventional copying-based restructuring and remapping-based restructuring that exploits a smart memory controller. We simulate six benchmark programs on a variety of input sizes and with a variety of restructuring optimizations. We find that employing a fixed strategy, e.g., only loop transformations or only copying-based restructuring, does not always deliver the best performance. We further find that for the benchmarks we examine, our cost-model chooses the best combination of restructuring optimizations the vast majority of the time, and yields performance within an average of 10% of optimal across all benchmarks and input sizes.