GCA: Global Congestion Awareness for Load Balance in Networks on Chip
As modern CMPs scale to ever increasing core counts, Networks on Chip (NoCs) are emerging as an interconnection fabric, enabling communication between components. While NoCs provide high and scalable bandwidth, current routing algorithms, such as dimension ordered routing, suffer from poor load balance, leading to reduced throughput and high latencies. Improving load balance, hence, is critical in future CMP designs where increased latency leads to wasted power and energy waiting for outstanding requests to resolve. Adaptive routing is a known technique to improve load balance, however, prior adaptive routing techniques either use local or regionally aggregated information to form their routing decisions. This paper proposes a new, light weight, adaptive routing algorithm for on chip routers based on global link state and congestion information, Global Congestion Awareness (GCA) GCA uses a simple, low complexity route calculation unit, to calculate paths to their destination without the myopia of local decisions, nor the aggregation of unrelated status information, found in prior designs. In particular GCA outperforms local adaptive routing by 26 percent, Regional Congestion Awareness (RCA) by 15 percent, and a recent competing adaptive routing algorithm, DAR, by 8 percent on average on realistic workloads.
IEEE Trans. Parallel Distributed Syst.