Nexus#: A Distributed Hardware Task Manager for Task-Based Programming Models

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 become...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings - IEEE International Parallel and Distributed Processing Symposium pp. 1129 - 1138
Main Authors: Dallou, Tamer, Elhossini, Ahmed, Juurlink, Ben, Engelhardt, Nina
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2015
Subjects:
Benchmark testing
data flow
Hardware
hardware support
hardware task scheduler
Multicore processing
parallel programming
Pipelines
Programming
Runtime
Scalability
task graph
task manager
ISSN:1530-2075
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1530-2075
DOI:10.1109/IPDPS.2015.79