As distributed AI workloads grow in scale, multi-GPU systems have become essential for training large models. Although techniques like kernel fusion and overlapping communication with computation help reduce delays, they also introduce irregular and transient traffic patterns that are difficult to model using existing tools. These techniques rely heavily on fine-grained synchronization and peer-to-peer communication, which place significant pressure on interconnect bandwidth and latency. In this work, we introduce Eidola, a scalable extension to the gem5 simulation framework that enables detailed modeling of inter-GPU communication traffic. The extension is scalable as our GPU model serves as a succinct eidolon, emulating the minimal characteristics needed for traffic modeling. Eidola uses annotated timing profiles from real applications to emulate peer-to-peer GPU writes with cycle-level precision. This allows researchers to simulate and analyze synchronization behavior across large multi-GPU configurations. The simulator supports configurable per-GPU traffic patterns and enables isolated performance analysis under different communication scenarios. We demonstrate Eidola's effectiveness by reproducing variability in fused kernel execution and by implementing a SyncMon-inspired synchronization mechanism, confirming reductions in polling-related memory traffic. Our results show that Eidola provides a flexible and scalable platform for studying inter-GPU communication and supports architectural exploration in modern distributed GPU systems.

翻译：随着分布式AI工作负载规模的扩展，多GPU系统已成为训练大模型的关键基础设施。尽管内核融合与计算通信重叠等技术有助于降低延迟，但它们也引入了现有工具难以建模的非规则瞬态流量模式。这些技术高度依赖细粒度同步与点对点通信，对互连带宽和延迟造成了显著压力。本文提出Eidola，一个可扩展的gem5仿真框架扩展组件，用于实现GPU间通信流量的精细建模。该扩展具有可扩展性，因为我们的GPU模型作为简洁的"模拟体"(eidolon)，仅模拟流量建模所需的最小特征。Eidola利用真实应用的注释时序配置文件，以时钟周期级精度模拟GPU点对点写入行为。这使得研究人员能够模拟并分析大规模多GPU配置下的同步行为。该仿真器支持可配置的单GPU流量模式，并能在不同通信场景下进行隔离性能分析。我们通过复现融合内核执行的变异性并实现SyncMon启发的同步机制，证实Eidola能有效减少与轮询相关的内存流量。实验结果表明，Eidola为研究GPU间通信提供了灵活可扩展的平台，并支持现代分布式GPU系统的架构探索。