Analytical Bounds for Optimal Tile Size Selection

In this paper, we introduce a novel approach to guide tile size selection by employing analytical models to limit empirical search within a subspace of the full search space. Two analytical models are used together: 1) an existing conservative model, based on the data footprint of a tile, which ignores intra-tile cache block replacement, and 2) an aggressive new model that assumes optimal cache block replacement within a tile. Experimental results on multiple platforms demonstrate the practical effectiveness of the approach by reducing the search space for the optimal tile size by 1,307× to 11,879× for an Intel Core-2Quad system; 358× to 1,978× for an Intel Nehalem system; and 45× to 1,142× for an IBM Power7 system. The execution of rectangularly tiled code tuned by a search of the subspace identified by our model achieves speed-ups of up to 1.40× (Intel Core-2 Quad), 1.28× (Nehalem) and 1.19× (Power 7) relative to the best possible square tile sizes on these different processor architectures. We also demonstrate the integration of the analytical bounds with existing search optimization algorithms. Our approach not only reduces the total search time from Nelder-Mead Simplex and Parallel Rank Ordering methods by factors of up to 4.95× and 4.33×, respectively, but also finds better tile sizes that yield higher performance in tuned tiled code.