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.

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure outsourcing of data processing to remote cloud services. Despite its promise of strong data privacy and security guarantees, FHE introduces a slowdown of up to five orders of magnitude as compared to the same computation using plaintext data. This overhead is presently a major barrier to the commercial adoption of FHE.

As GPUs continue to grow in popularity for accelerating demanding applications, such as high-performance computing and machine learning, GPU architects need to deliver more powerful devices with updated instruction set architectures (ISAs) and new microarchitectural features. The introduction of the AMD RDNA architecture is one example where the GPU architecture was dramatically changed, modifying the underlying programming model, the core architecture, and the cache hierarchy. To date, no publicly-available simulator infrastructure can model the AMD RDNA GPU, preventing researchers from exploring new GPU designs based on the state-of-the-art RDNA architecture.

The rapidly growing popularity and scale of data-parallel workloads demand a corresponding increase in raw computational power of Graphics Processing Units (GPUs). As single-GPU platforms struggle to satisfy these performance demands, multi-GPU platforms have started to dominate the high-performance computing world. The advent of such systems raises a number of design challenges, including the GPU microarchitecture, multi-GPU interconnect fabric, runtime libraries, and associated programming models. The research community currently lacks a publicly available and comprehensive multi-GPU simulation framework to evaluate next-generation multi-GPU system designs.