Communication and congestion aware run-time task mapping on heterogeneous MPSoCs

Khajekarimi, Elyas; Hashemi, Mahmoud Reza

doi:10.1109/cads.2012.6316432

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…The weight on an edge represents the bandwidth requirement of the communication between any two tasks. These task graphs may contain both unidirectional and bidirectional edges, signifying the nature of the task dependencies [10,22]. Unidirectional edges indicate a one-way dependency between tasks.…”

Section: Modeling Of Application In the Form Of Task Graphmentioning

confidence: 99%

Cluster Based Adaptive Multi-Voltage Scaling Dynamic Task Mapping for WNoC and HWNoC

Ghosh,

Agarwal,

Sinha

et al. 2024

Preprint

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Wireless Network-on-Chip (WNoC) and Hybrid Wireless Network-on-Chip (HWNoC) architectures are promising solutions for future high-performance computing systems. However, WNoC consumes significant power, while HWNoC experiences congestion over the wireless link. Several state-of-the-art task mapping algorithms have been proposed to reduce power consumption and congestion over wireless links. However, these existing task mapping algorithms face challenges related to hotspots creation, sub-optimal utilization of wireless links, and also overlook idle core power reduction strategy. Additionally, each of the existing task mapping algorithms is designed for a specific architecture, either WNoC or HWNoC. To address these challenges we propose a novel task mapping algorithm called Cluster-Based Adaptive Multi-Voltage Scaling (CB-AMS). This algorithm dynamically maps tasks to clusters while performing multi-voltage scaling based on workload to significantly reduce power consumption and congestion over wireless links. A new cluster selection strategy is also proposed in CB-AMS to address the hotspot creation issue. CB-AMS is designed to be used in both WNoC and HWNoC architecture. Experimental results show that CB-AMS significantly reduces power consumption by 41% for WNoC and by 15-20% for HWNoC compared to state-of-the-art task mapping algorithms. Experimental results also validate that CB-AMS achieves better congestion control in HWNoC architecture by reducing latency by 3.6-5.5% compared to existing task mapping algorithms. Our experimental analysis has demonstrated that CB-AMS outperforms the current algorithms and delivers significant power reduction and improved congestion control for both WNoC and HWNoC architectures.

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Section: Modeling Of Application In the Form Of Task Graphmentioning

confidence: 99%