Title :
Nexus#: A Distributed Hardware Task Manager for Task-Based Programming Models
Author :
Dallou, Tamer ; Elhossini, Ahmed ; Juurlink, Ben ; Engelhardt, Nina
Author_Institution :
Embedded Syst. Archit., Tech. Univ. Berlin, Berlin, Germany
Abstract :
In the era of multicore systems, it is expected that the number of cores that can be integrated on a single chip will be 3-digit. The key to utilize such a huge computational power is to extract the very fine parallelism in the user program. This is non-trivial for the average programmer, and becomes very hard as the number of potential parallel instances increases. Task-based programming models such as OmpSs are promising, since they handle the detection of dependencies and synchronization for the programmer. However, state-of-the-art research shows that task management is not cheap, and introduces a significant overhead that limits the scalability of OmpSs. Nexus# is a hardware accelerator for the OmpSs runtime system, which dynamically monitors dependencies between tasks. It is fully synthesizable in VHDL, and has a distributed task graph model to achieve the best scalability. Supporting tasks with arbitrary number of parameters and any dependency pattern, Nexus# achieves better performance than Nanos, the official OmpSs runtime system, and scales well for the H264dec benchmark with very fine grained tasks, among other benchmarks from the Starbench suite.
Keywords :
hardware description languages; multiprocessing systems; parallel programming; resource allocation; Nexus distributed hardware task manager; OmpS programming model; Starbench suite; VHDL; multicore systems; parallel instance; programmer dependency; programmer synchronization; task-based programming model; task-based programming models; user program parallelism; very high scale description language; Benchmark testing; Hardware; Multicore processing; Pipelines; Programming; Runtime; Scalability; data flow; hardware support; hardware task scheduler; parallel programming; task graph; task manager;
Conference_Titel :
Parallel and Distributed Processing Symposium (IPDPS), 2015 IEEE International
Conference_Location :
Hyderabad
DOI :
10.1109/IPDPS.2015.79