提出Bine树优化大规模计算通信局部性，显著减少全局链路流量。
Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality
- 采用新型Bine树结构改进集体通信算法
- 全局链路流量减少最高达33%，性能提升最高5倍
- 适用于超算系统，尤其适合高负载网络环境
在大型高性能计算系统中，通信局部性对集体操作性能至关重要，尤其是在过度占用的网络中，节点组内部全连接但通过全局连接稀疏互联。本文提出Bine（二进制负二进制）树，一类改进通信局部性的集体算法。Bine树在保持二项式树与蝴蝶结构通用性的同时，将全局链路通信量减少高达33%。我们实现了八个基于Bine树的集体操作，并在四台大规模超算上进行了评估，涵盖Dragonfly、Dragonfly+、过度占用胖树和环形拓扑，结果表明在不同向量大小和节点数下，均实现最高5倍的加速比，并持续降低全局链路通信量。
原文摘要 · Abstract (English)
Communication locality plays a key role in the performance of collective operations on large HPC systems, especially on oversubscribed networks where groups of nodes are fully connected internally but sparsely linked through global connections. We present Bine (binomial negabinary) trees, a family of collective algorithms that improve communication locality. Bine trees maintain the generality of binomial trees and butterflies while cutting global-link traffic by up to 33%. We implement eight Bine-based collectives and evaluate them on four large-scale supercomputers with Dragonfly, Dragonfly+, oversubscribed fat-tree, and torus topologies, achieving up to 5x speedups and consistent reductions in global-link traffic across different vector sizes and node counts.
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