← 研踪 PaperTrailarXiv:2611.11811 · v1
Rethinking Adaptive Routing on Dragonfly
首次提交 2026-11-09 11:17
版本更新 2026-11-09 11:17
雷达观测 2026-11-10 13:18
摘要
The Dragonfly topology is widely deployed in leading supercomputers, where adaptive routing is crucial for high-performance interconnection. Despite years of research, a dilemma remains: state-of-the-art adaptive routing algorithms achieve excellent performance but demand hardware budgets far beyond what real router chips can afford -- prohibitive buffers, logically dependent floating-point operations, or exhaustive path-set traversals -- whereas realizable algorithms deliver unsatisfactory performance. Furthermore, existing algorithms fail to exploit the abundant almost-minimal paths in Dragonfly: when only the local channels of a minimal path are congested, they reroute packets to significantly longer global non-minimal paths, wasting global bandwidth and increasing latency. To address both limitations, we analyze intra-group path contention and propose the Decoupled Incremental Adaptive Routing (DIAR) algorithm, which decouples routing into global path selection at the source and incremental local detours onto almost-minimal paths, guaranteeing deadlock freedom without additional virtual channels, escape rings, or turn models. Requiring only a few comparators, shifters, adders, and multiplexers, DIAR reduces buffer overhead by up to four orders of magnitude compared with state-of-the-art algorithms, while achieving comparable or superior performance across all evaluated scenarios: relative to the strongest realizable adaptive routing algorithm in each scenario, DIAR improves saturation throughput by up to 30.0% across synthetic traffic patterns and improves performance by up to 34.9% (13.2% on average) across HPC workloads.
作者备注：12 pages, 9 figures. Preliminary version. An updated final version will follow, and the source code will be open-sourced
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v1 · 2026-11-09 11:17 · 最新观测版本
Rethinking Adaptive Routing on Dragonfly
The Dragonfly topology is widely deployed in leading supercomputers, where adaptive routing is crucial for high-performance interconnection. Despite years of research, a dilemma remains: state-of-the-art adaptive routing algorithms achieve excellent performance but demand hardware budgets far beyond what real router chips can afford -- prohibitive buffers, logically dependent floating-point operations, or exhaustive path-set traversals -- whereas realizable algorithms deliver unsatisfactory performance. Furthermore, existing algorithms fail to exploit the abundant almost-minimal paths in Dragonfly: when only the local channels of a minimal path are congested, they reroute packets to significantly longer global non-minimal paths, wasting global bandwidth and increasing latency. To address both limitations, we analyze intra-group path contention and propose the Decoupled Incremental Adaptive Routing (DIAR) algorithm, which decouples routing into global path selection at the source and incremental local detours onto almost-minimal paths, guaranteeing deadlock freedom without additional virtual channels, escape rings, or turn models. Requiring only a few comparators, shifters, adders, and multiplexers, DIAR reduces buffer overhead by up to four orders of magnitude compared with state-of-the-art algorithms, while achieving comparable or superior performance across all evaluated scenarios: relative to the strongest realizable adaptive routing algorithm in each scenario, DIAR improves saturation throughput by up to 30.0% across synthetic traffic patterns and improves performance by up to 34.9% (13.2% on average) across HPC workloads.
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