Destination-Based Congestion Awareness for Adaptive Routing in 2D Mesh Networks

摘要

The choice of routing algorithm plays a vital role in the performance of on-chip interconnection networks. Adaptive routing is appealing because it offers better latency and throughput than oblivious routing, especially under nonuniform and bursty traffic. The performance of an adaptive routing algorithm is determined by its ability to accurately estimate congestion in the network. In this regard, maintaining global congestion state using a separate monitoring network offers better congestion visibility into distant parts of the network compared to solutions relying only on local congestion. However, the main challenge in designing such routing schemes is to keep the logic and bandwidth overhead as low as possible to fit into the tight power, area, and delay budgets of on-chip routers. In this article, we propose a minimal destination-based adaptive routing strategy (DAR), where every node estimates the delay to every other node in the network, and routing decisions are based on these per-destination delay estimates. DAR outperforms Regional Congestion Awareness (RCA), the best previously known adaptive routing algorithm that uses nonlocal congestion state. The performance improvement is brought about by maintaining fine-grained per-destination delay estimates in DAR that are more accurate than regional congestion metrics measured in RCA. The increased accuracy is a consequence of the fact that the per-destination delay estimates are not corrupted by congestion on links outside the admissible routing paths to the destination. A scalable version of DAR, referred to as SDAR, is also proposed for minimizing the overheads associated with DAR in large network topologies. We show that DAR outperforms local adaptive routing by up to 79% and RCA by up to 58% in terms of latency on SPLASH-2 benchmarks. DAR and SDAR also outperform existing adaptive and oblivious routing algorithms in latency and throughput under synthetic traffic patterns on 8 × 8 and 16 × 16 mesh topologies, respectively.