Hardware-Based Task Dependency Resolution for the StarSs Programming Model

Recently, several programming models have been proposed that try to relieve parallel programming. One of these programming models is StarSs. In StarSs, the programmer has to identify pieces of code that can be executed as tasks, as well as their inputs and outputs. Thereafter, the runtime system (RTS) determines the dependencies between tasks and schedules ready tasks onto worker cores. Previous work has shown, however, that the StarSs RTS may constitute a bottleneck that limits the scalability of the system and proposed a hardware task management system called Nexus to eliminate this bottleneck. Nexus has several limitations, however. For example, the number of inputs and outputs of each task is limited to a fixed constant and Nexus does not support double buffering. In this paper we present Nexus++ that addresses these as well as other limitations. Experimental results show that double buffering achieves a speedup of 54×/143× with/without modeling memory contention respectively, and that Nexus++ significantly enhances the scalability of applications parallelized using StarSs.