ASLink: Modeling Multi-GPU Execution in Accel-Sim
Christin David Bose, Cesar Avalos, Junrui Pan, Yechen Liu, Mahmoud Khairy, Clay Hughes, Timothy G. Rogers · 2025
Graphical processing units (GPUs) are widely used in numerous modern application domains, including modeling and simulation, machine learning, and data analytics. Many applications such as recommendation models and graph neural networks benefit from the use of multiple GPUs to scale up the size of the workload and increase throughput. While current open-sourced GPU architectural simulators can model multiGPU workloads, doing so remains inefficient and challenging, limiting their broader applicability across various application domains. On the other hand, simulation tools used by the industry are often closed-source, thus hindering efforts to democratize architectural research. This paper proposes an open-source simulator design, ASLink, that extends Accel-sim to support multiGPU configurations. We highlight the limitations of popular state-of-the-art GPU architecture simulators and propose mechanisms to improve user experience and modeling fidelity in multi-GPU systems. Finally, we validate our proposed infrastructure against kernels representative of real world workloads.