Polygonal Iteration Space Partitioning using the Polyhedral Model

Loop-nests in most scientific applications perform repetitive operations on array(s) and account for most of the program execution time. Traditional loop transformations, such as tiling, leverage data locality and maximize program performance on modern micro-architectures. These transformations, however, effectively maximize performance of programs when loop-nests exhibit uniform reuse patterns.In this thesis, a new loop transformation is presented to target loop-nests with non-uniform reuse patterns. The proposed loop transformation uses the norms of the Polyhedral Model to represent the loop-nests and then leverages such a representation to partition the iteration space into polygonally shaped partitions. These partitions optimize locality resulting in an improvement in performance for both serial and parallel execution. Improving locality in parallel execution requires selective mapping of partitions on threads based on the type of reuse these partitions exhibit.The experiments on certain loop-nests show that a significant portion of the achievable performance is missed when applying the traditional loop transformations. Compared to state-of-the-art Polyhedral Model frameworks, the transformation shows a consistent performance speedup in serial (up to 1.2x over Polly) and parallel (up to 3.17x over PLuTo) executions for certain loop-nests with non-uniform reuse patterns