The diversity of communication paths in a network, especially non-minimal paths, is a key enabler of performance at extreme scales. We present EvalNet, a toolchain for scalable generation and analysis of over 25 important network topologies, such as Slim Fly, PolarFly, and Orthogonal Fat Trees, with a strong focus on path diversity metrics. EvalNet provides an extensive and fine-grained analysis of shortest and non-shortest paths, including their multiplicities, lengths, and interference. It supports exact measurement and visualization of bandwidth and throughput between every router pair, enabling unprecedented insight into routing potential. EvalNet also includes detailed models for construction cost and power consumption, and interfaces seamlessly with established simulators, which we tune to support large-scale evaluations on low-cost hardware. Using EvalNet, we deliver the widest and most comprehensive path diversity study to date, demonstrating how path diversity underpins throughput and scalability, and facilitating progress towards new frontiers in extreme-scale network design.

翻译：网络中通信路径的多样性，尤其是非最短路径，是实现极大规模性能的关键因素。我们提出EvalNet，一个用于可扩展生成与分析超过25种重要网络拓扑（如Slim Fly、PolarFly和正交胖树）的工具链，重点关注路径多样性指标。EvalNet提供了对最短路径与非最短路径的广泛细粒度分析，包括其多重性、长度和干扰。它支持每对路由器之间带宽和吞吐量的精确测量与可视化，从而为路由潜力提供前所未有的洞察。EvalNet还包含详细的构建成本与功耗模型，并与现有模拟器无缝对接，我们对其进行了调优以支持在低成本硬件上进行大规模评估。借助EvalNet，我们完成了迄今为止最广泛、最全面的路径多样性研究，展示了路径多样性如何支撑吞吐量与可扩展性，并推动极大规模网络设计迈向新前沿。