TrioSim: A Lightweight Simulator for Large-Scale DNN Workloads on Multi-GPU Systems
Ying Li, Yuhui Bao, Gongyu Wang, Xinxin Mei, Pranav Vaid, Anandaroop Ghosh, Adwait Jog, Darius Bunandar, Ajay M. Joshi, Yifan Sun · 2025
Deep Neural Networks (DNNs) have become increasingly capable of performing tasks ranging from image recognition to content generation.The training and inference of DNNs heavily rely on GPUs, as GPUs' massively parallel architecture delivers extremely high computing capability.With the growing complexity of DNNs and the size of training datasets, training DNNs with a large number of GPUs is becoming a prevalent strategy.Researchers have been exploring how to design software and hardware systems for GPU farms to achieve the best utilization, efficiency, and DNN accuracy during training or inference.However, when designing and deploying such systems, designers usually rely on testing on physical hardware platforms equipped with many GPUs, incurring high costs that are almost prohibitive for system designers to test different configurations and designs, even for highly resourceful companies.While an alternative solution is to test on GPU simulators, they are often too slow for these large-scale systems and depend on profiling details collected from real distributed systems to initiate the simulation.To address these challenges, we present TrioSim, a novel lightweight simulator for DNNs on multi-GPU systems.TrioSim combines performance modeling techniques and simulation methods to achieve high flexibility, high simulation speed, and * Part of this work was done while Yuhui Bao and Pranav Vaid were interns at Lightmatter.