Mix and Match: Reorganizing Tasks for Enhancing Data Locality

Abstract

Application programs that exhibit strong locality of reference lead to minimized cache misses and better performance in different architectures. In this paper, we target task-based programs, and propose a novel compiler-based approach that consists of four complementary steps. First, we partition the original tasks in the target application into sub-tasks and build a data reuse graph at a sub-task granularity. Second, based on the intensity of temporal and spatial data reuses among sub-tasks, we generate new tasks where each such (new) task includes a set of sub-tasks that exhibit high data reuse among them. Third, we assign the newly-generated tasks to cores in an architecture-aware fashion with the knowledge of data location. Finally, we re-schedule the execution order of sub-tasks within new tasks such that sub-tasks that belong to different tasks but share data among them are executed in close proximity in time. The experiments show that, when targeting a state of the art manycore system, our compiler-based approach improves the performance of 10 multithreaded programs by 23.4% on average.