Global Task Data-Dependencies in PGAS Applications

Recent years have seen the emergence of two independent programming models challenging the traditional two-tier combination of message passing and thread-level work-sharing: partitioned global address space (PGAS) and task-based concurrency. In the PGAS programming model, synchronization and communication between processes are decoupled, providing significant potential for reducing communication overhead. At the same time, task-based programming allows to exploit a large degree of shared-memory concurrency. The inherent lack of fine-grained synchronization in PGAS can be addressed through fine-grained task synchronization across process boundaries. In this work, we propose the use of task data dependencies describing the data-flow in the global address space to synchronize the execution of tasks created in parallel on multiple processes. We present a description of the global data dependencies, describe the necessary interactions between the distributed scheduler instances required to handle them, and discuss our implementation in the context of the DASH PGAS framework. We evaluate our approach using the Blocked Cholesky Factorization and the LULESH proxy app, demonstrating the feasibility and scalability of our approach.