Data Layout Transformation for Enhancing Data Locality on NUCA Chip Multiprocessors

Abstract : With increasing numbers of cores, future CMPs (Chip Multi-Processors) are likely to have a tiled architecture with a portion of shared L2 cache on each tile and a bank-interleaved distribution of the address space. Although such an organization is effective for avoiding access hot-spots, it can cause a significant number of non-local L2 accesses for many commonly occurring regular data access patterns. In this paper we develop a compile-time framework for data locality optimization via data layout transformation. Using a polyhedral model, the program's localizability is determined by analysis of its index set and array reference functions, followed by non-canonical data layout transformation to reduce non-local accesses for localizable computations. Simulation-based results on a 16-core 2D tiled CMP demonstrate the effectiveness of the approach. The developed program transformation technique is also useful in several other data layout transformation contexts.