Dissecting and Modeling the Architecture of Modern GPU Cores
Rodrigo Huerta, Mojtaba Abaie Shoushtary, José-Lorenzo Cruz, Antonio M. González · 2025
GPUs are the most popular platform for accelerating HPC workloads, such as artificial intelligence and science simulations.However, most microarchitectural research in academia relies on simulators that model GPU core architectures based on designs that are more than 15 years old, and differ significantly from modern core architectures.This work reverse engineers the architecture of modern NVIDIA GPU cores, unveiling key aspects of its design and the important role of the compiler in some of its main components.In particular, it reveals how the issue logic works, the structure of the register file and its associated cache, multiple features of the instruction and data memory pipelines.When modeling all these discovered microarchitectural details in a state-of-the-art simulation framework, we show that its accuracy is significantly improved, achieving a 20.58% reduction in mean absolute percentage error (MAPE) on average, which results in a 13.45% MAPE on average with respect to real modern hardware.In addition, we show that the software-based dependence management mechanism included in modern NVIDIA GPUs outperforms a hardware mechanism based on scoreboards in terms of performance and area.