Sun, X, Dong, Y, Chen, J et al. (1 more author) (2020) Network-on-Chip Aware Task Mappings. In: ACA 2020: Advanced Computer Architecture. 13th Conference on Advanced Computer Architecture (ACA 2020), 13-15 Aug 2020, Kunming, China (Online). Springer Nature , pp. 135-149. ISBN 978-981-15-8134-2
Abstract
Energy and power density have forced the industry to introduce many-cores where a large number of processor cores are integrated into a single chip. In such settings, the communication latency of the network on chip (NoC) could be performance bottleneck of a multi-core and many-core processor. Unfortunately, existing approaches for mapping the running tasks to the underlying hardware resources often ignore the impact of the NoC, leading to sub-optimal performance and energy efficiency. This paper presents a novel approach to allocating NoC resource among running tasks. Our approach is based on the topology partitioning of the shared routers of the NoC. We evaluate our approach by comparing it against two state-of-the-art methods using simulation. Experimental results show that our approach reduces the NoC communication latency by 5.19% and 2.99%, and the energy consumption by 17.94% and 12.68% over two competitive approaches.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Singapore Pte Ltd. 2020 This is an author produced version of a conference paper published in ACA 2020: Advanced Computer Architecture. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Network on Chip; Performance optimization; Many-cores |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Sep 2020 11:58 |
Last Modified: | 05 Sep 2021 00:38 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1007/978-981-15-8135-9_10 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165633 |