C2C-Explorer：用于大语言模型云计算系统中芯片间互连架构的探索框架
C2C-Explorer: An Exploration Framework for Chip-to-Chip Interconnect Architectures in LLM Cloud Computing Systems
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中文总结 AI 辅助
C2C-Explorer是一种自适应贝叶斯DSE框架，整合流量生成器、可扩展互连模拟器与评估器，用于探索LLM云计算系统的C2C互连架构，在DeepSeek-R1-671B负载中可提升有效吞吐量并降低内存占用。
中文摘要 AI 辅助
大语言模型（LLM）的规模扩大要求计算系统具备多处理器芯片架构，这提升了芯片间（C2C）通信的重要性。然而，为LLM工作负载设计高效的C2C硬件架构面临三大关键挑战：生成符合LLM特性的真实C2C流量、准确模拟大规模硬件级通信，以及高效探索呈指数级增长的C2C设计空间。我们提出C2C-Explorer，这是一种自适应贝叶斯设计空间探索（DSE）框架，它将LLM工作负载驱动的流量生成器、可扩展互连模拟器（支持交换式/全网状拓扑，最多适配512个芯片）以及指标引导评估器整合到工作负载到硬件的优化流水线中，实现了在真实LLM工作负载下的系统性C2C架构协同设计。经基于FPGA的C2C原型验证，该C2C模拟器在不同流量模式下实现了2.46%-8.23%的端到端时序误差；其混合周期与事件模型相比纯周期精确基线，可将大规模模拟速度提升最高达7.8倍。将其应用于32-XPU的DeepSeek-R1-671B推理工作负载时，C2C-Explorer识别出的配置使有效吞吐量（goodput）提升44.1%，内存占用降低98.4%。C2C-Explorer为开源项目，可通过指定URL获取。
英文摘要
The scaling-up of large language models (LLMs) necessitates computing systems to have multi-processor-chip architectures, elevating the importance of chip-to-chip (C2C) communication. However, designing efficient C2C hardware architectures for LLM workloads faces three key challenges: generating realistic LLM-specific C2C traffic, accurately simulating hardware-level communication at scale, and efficiently exploring the exponentially large C2C design space. We propose C2C-Explorer, an adaptive Bayesian DSE framework that integrates a LLM-workload-driven traffic generator, a scalable interconnect simulator (switch/full-mesh, up to 512 chips), and a metric-guided evaluator into a workload-to-hardware optimization pipeline, enabling systematic C2C architectural co-design under realistic LLM workloads. Validated against FPGA-based C2C prototypes, the C2C simulator achieves 2.46-8.23% end-to-end timing error across diverse traffic patterns. Its hybrid cycle and event model further accelerates large-scale simulation by up to 7.8$\times$ over a pure cycle-accurate baseline. Applied to a 32-XPU DeepSeek-R1-671B inference workload, C2C-Explorer identifies configurations that improve goodput by 44.1% and reduce memory by 98.4%. C2C-Explorer is open-source and available at https://github.com/Selinaee/C2C-Explorer.
