Data Movement Aware Computation Partitioning

Data access costs dominate the execution times of most parallel applications and they are expected to be even more important in the future. To address this, recent research has focused on Near Data Processing (NDP) as a new paradigm that tries to bring computation to data, instead of bringing data to computation (which is the norm in conventional computing). This paper explores the potential of compiler support in exploiting NDP in the context of emerging many core systems. To that end, we propose a novel compiler algorithm that partitions the computations in a given loop nest into sub computations and schedules the resulting sub computations on different cores with the goal of reducing the distance-to-data on the on-chip network. An important characteristic of our approach is that it exploits NDP while taking advantage of data locality. Our experiments with 12 multi-threaded applications running on a state of-the-art commercial many core system indicate that the proposed compiler-based approach significantly reduces data movements on the on-chip network by taking advantage of NDP, and these benefits lead to an average execution time improvement of 18.4%.