Orrery：基于SimGrid的GPU直接MPI通信建模与仿真
Orrery: Modeling and Simulation of GPU-direct MPI Communication with SimGrid
AI总结：
该研究提出基于SimGrid SMPI的Orrery拦截层，实现无需物理GPU的GPU感知MPI通信仿真，经微基准测试和雅可比求解器验证，可复现关键通信机制。
摘要：
HPC应用仿真是分析系统设计权衡、支撑软硬件协同设计的重要工具。现代超级计算机日益依赖GPU加速，MPI实现已演进为支持GPU感知通信，可实现GPU数据直接传输，因此对这些机制建模是当前系统上HPC应用仿真的关键。本文提出Orrery，这是构建于SimGrid的SMPI之上的拦截层，利用现有集合通信实现和基于流的仿真来建模GPU感知MPI通信。Orrery会拦截GPU内存分配、内核启动、数据传输及MPI操作，并根据目标系统规格在SimGrid中对其执行过程建模，无需物理GPU即可实现仿真。我们使用支持CUDA的MPI微基准测试和MUSICA超级计算机的最多32个节点上的雅可比求解器对Orrery进行验证，结果表明它能复现关键的GPU感知MPI机制。
英文摘要：
Simulation of HPC applications is an essential tool for analyzing system design trade-offs and enabling effective hardware--software co-design. As modern supercomputers increasingly rely on GPU acceleration, MPI implementations have evolved to support GPU-aware communication, enabling direct GPU data transfers. Modeling these mechanisms is therefore critical for simulating HPC applications on current systems. In this paper, we introduce Orrery, an interposition layer built on SimGrid's SMPI to model GPU-aware MPI communication using existing collective implementations and flow-based simulation. Orrery intercepts GPU memory allocations, kernel launches, data transfers, and MPI operations, and models their execution in SimGrid according to target system specifications, enabling simulation without requiring physical GPUs. We validate Orrery using CUDA-aware MPI microbenchmarks and a Jacobi solver on up to 32 nodes of the MUSICA supercomputer and demonstrate that it reproduces key GPU-aware MPI mechanisms.
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