UNIFERENCE用仿真框架统一开发部署分布式AI模型，提升可复现性。
UNIFERENCE: A Discrete Event Simulation Framework for Developing Distributed AI Models
- 基于离散事件仿真，仅在通信时同步，避免回滚且保持因果顺序。
- 实测运行时间预测准确率达98.6%，跨多种硬件和后端表现稳定。
- 与PyTorch Distributed无缝集成，代码可直接从仿真部署到真实设备。
分布式推理算法的开发与评估因缺乏标准化工具而困难重重，现有研究常依赖临时搭建的测试环境或专有基础设施，导致结果难以复现，并限制对假设性硬件或网络配置的探索。我们提出UNIFERENCE，一个面向分布式AI模型开发、基准测试与部署的离散事件仿真（DES）框架。该框架通过轻量级逻辑进程建模设备与网络行为，仅在通信原语上同步，避免回滚同时保证因果顺序。它与PyTorch Distributed无缝集成，使同一代码库可直接从仿真过渡至真实部署。评估表明，UNIFERENCE在多种后端与硬件配置下，运行时间预测准确率高达98.6%。通过连接仿真与部署，UNIFERENCE为研究分布式推理算法及未来系统设计（如高性能集群到边缘设备）提供了一个可访问、可复现的平台。框架已开源：https://github.com/Dogacel/Uniference。
原文摘要 · Abstract (English)
Developing and evaluating distributed inference algorithms remains difficult due to the lack of standardized tools for modeling heterogeneous devices and networks. Existing studies often rely on ad-hoc testbeds or proprietary infrastructure, making results hard to reproduce and limiting exploration of hypothetical hardware or network configurations. We present UNIFERENCE, a discrete-event simulation (DES) framework designed for developing, benchmarking, and deploying distributed AI models within a unified environment. UNIFERENCE models device and network behavior through lightweight logical processes that synchronize only on communication primitives, eliminating rollbacks while preserving the causal order. It integrates seamlessly with PyTorch Distributed, enabling the same codebase to transition from simulation to real deployment. Our evaluation demonstrates that UNIFERENCE profiles runtime with up to 98.6% accuracy compared to real physical deployments across diverse backends and hardware setups. By bridging simulation and deployment, UNIFERENCE provides an accessible, reproducible platform for studying distributed inference algorithms and exploring future system designs, from high-performance clusters to edge-scale devices. The framework is open-sourced at https://github.com/Dogacel/Uniference.
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