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Adaptive distribution of control messages for improving bandwidth utilization in multiple NoC

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Abstract

The advancement of networks-on-chip (NoCs) is noteworthy as the number of cores increases. The bandwidth demand has grown steadily as network traffic has increased owing to high-workload applications. The NoC traffic broadly divided into control messages and data messages in which data messages are bigger in size. As NoC channel bandwidth sets in proportion to the size of the data messages, the NoC bandwidth remains underutilized during control messages transmission. This adversely affects NoC power and performance efficiency. In modern NoC architectures, multiple NoC is popular to efficiently utilize NoC bandwidth because it offers more than one physical channel for traffic communication. The conventional multiple-NoC architectures statically distribute traffic between the NoCs. This significantly affects the power-performance metrics. We have observed up to fivefold variation in energy efficiency during the analysis of static traffic distribution for multiple NoC. In this paper, we propose an adaptive distribution of control messages for multiple NoC to improve bandwidth utilization. The traversal of control messages switch between the NoC networks according to the runtime utilization of networks. The proposed adaptive distribution of control messages improves energy efficiency up to \(72.7\%\) and \(66.9\%\) on average over single-NoC and static traffic distribution in multiple NoC, respectively. The link utilization also improves by \(1.37\times\) and \(40\%\) on average over single-NoC and conventional static traffic distribution, respectively. Thus, the proposed adaptive distribution overcomes the implications of static traffic distribution.

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Data Availability Statement

Not applicable.

Notes

  1. Single-Chip Cloud Computer.

  2. bits per second.

  3. Giga Bytes per second.

  4. Tera bits per second.

  5. The number of messages.

  6. The dotted red box exhibits volume of control messages, whereas blue-lined box (the rightmost column of the table) highlights the percentage variation in result metric.

  7. We shall interchangeably use the term percentage/fraction/ratio/proportion.

  8. Least Recently Used (LRU) replacement policy.

  9. The names of fine-grained messages indicate message class, message type, and cache association.

  10. Less-frequent messages.

  11. Gem5 simulator facilitates various CPU models, Instruction Set Architecture (ISAs), caches, and cache-coherence protocol models.

  12. In full system simulation, the hardware of computer system is simulated at the level of the details such that the complete software stacks from real systems can run on the simulator.

  13. router connects with rest of the routers via four links {N, S, E, W} in North, South, East, and West directions specific to mesh topology.

  14. RMS-recognition mining synthesis.

  15. The data patterns further dependent on number of cores, cache size, and cache line size.

  16. FLoating pOint oPerations: this is computation unit that is used in high-performance computing for improving accuracy in results.

  17. these are the synchronization primitives.

  18. The geometric mean is the standard way to represent the results for averaging normalized results [61].

  19. for the classification of the benchmarks, please refer Table 10.

  20. One message transmits through more than one NoC.

  21. Each NoC carries different types of messages.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, India

    Sonal Yadav

  2. Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India

    Vijay Laxmi & Manoj Singh Gaur

  3. Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India

    Hemangee Kapoor

  4. Department of Computer Science and Engineering, Indian Institute of Information Technology Kota, Jaipur, India

    Amit Kumar

  5. Indian Institute of Technology Jammu, Jammu, India

    Manoj Singh Gaur

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The first author originated the idea, implemented it and wrote the paper. The second, third, and fourth authors all helped to conceptualize the idea, organize the manuscript, and analyze the results. The fifth author contributes to the revision of the manuscript.

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Appendix A: Message distribution and cache state transitions

Appendix A: Message distribution and cache state transitions

Table 15 in this appendix summarizes 41 alternative message distributions on dual-NoC that are executed for each PARSEC benchmark application described in Table 3 (Sect. 3). Figures 9 and 10 are tabular representations of cache state transition graphs, as discussed in Sect.  4, Figs. 1 and 2, respectively.

See Figs. 9 and 10.

Table 15 Each row except the first row indicates static message distribution out of 41 combinations. The static message distribution is Not Applicable (NA) for the single-NoC (first row)
Full size table
Fig. 9
figure 9

Tabular representation of L\(_1\) cache state transitions and respective messages. The rows and columns of table represent the stable and transient states of the L\(_1\) cache. Messages in each table cell generated as the cache state transitions from one state to another

Full size image
Fig. 10
figure 10

Tabular representation of L\(_2\) cache state transitions and respective messages. The rows and columns of table represent the stable and transient states of the L\(_2\) cache. Messages in each table cell generated as the cache state transitions from one state to another

Full size image

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Yadav, S., Laxmi, V., Kapoor, H. et al. Adaptive distribution of control messages for improving bandwidth utilization in multiple NoC. J Supercomput 79, 17208–17246 (2023). https://doi.org/10.1007/s11227-023-05208-0

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