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GCA:Global Congestion Awareness for Load Balance in Networks-on-Chip

机译：GCA：片上网络中负载平衡的全球拥塞意识

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摘要

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.
机译：随着现代CMP扩展到不断增加的核心数量，片上网络（NoC）逐渐成为一种互连结构，支持组件之间的通信。尽管NoC提供了高且可扩展的带宽，但是当前的路由算法（如按维度排序的路由）会承受较差的负载平衡，从而导致吞吐量降低和延迟时间延长。因此，在未来的CMP设计中，提高负载平衡至关重要，因为延迟增加会导致等待解决未决请求的功率和能量浪费。自适应路由是改善负载平衡的已知技术，但是，现有的自适应路由技术使用本地或区域聚合的信息来形成其路由决策。本文提出了一种新的，基于全局链路状态和拥塞信息的片上路由器的轻量级自适应路由算法，即全局拥塞意识（GCA）。 GCA使用简单，低复杂度的路由计算单元来计算到达目的地的路径，而无需本地决策的近视，也不会聚合先前设计中发现的无关状态信息。特别是在实际工作负载方面，GCA的性能比本地自适应路由要高出26％，而区域拥塞意识（RCA）则要高出15％，而最新竞争的自适应路由算法DAR则要高出8％。

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