Architecting the Next Generation of Asynchronous, Distributed GPUs for the AI Era

By Junrui Pan, Weili An, Cesar Avalos Baddouh, Christin David Bose, Ni Kang, Aaron Barnes, Ahmad Alawneh, Fangjia Shen, Yechen Liu, Anusuya Nallathambi, Atthin Chandrashekar, Timothy G. Rogers
Purdue University, West Lafayette, IN, USA

Abstract

The rapid evolution of machine learning workloads has fundamentally transformed GPU hardware, driving architectures toward Multi-Chip Module (MCM) topologies, asynchronous execution primitives, and persistent, multi-phase kernel behaviors. Despite these shifts, cycle-level simulation infrastructure has lagged behind, lacking the native capability to model the physical non-uniformity of modern GPUs alongside the massive scale of state-of-the-art AI workloads. To bridge this gap, we present a cycle-level simulation framework designed to accurately model modern GPU generations, including Ampere, Hopper, and Blackwell. Rigorously validated against physical silicon, the simulator achieves a 99% Pearson correlation coefficient and a 13.4% mean absolute cycle error on the H100 GPU. Utilizing this infrastructure, we conduct architectural case studies to evaluate emerging design trajectories, including chiplet topology scaling, expanded SRAM capacity and bandwidth, and inter-GPU prefetching strategies.

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