Towards Scalable GPU System with Silicon Photonic Chiplet

GPU-based computing has emerged as a predominant solution for high-performance computing and machine learning applications. The continuously escalating computing demand fore-sees a requirement for larger-scale GPU systems in the future. However, this expansion is constrained by the finite number of transistors per die. Although chip let technology shows potential for building large-scale systems, current chiplet interconnection technologies suffer from limitations in both bandwidth and en-ergy efficiency. In contrast, optical interconnect has ultra-high bandwidth and energy efficiency, and thereby is promising for constructing chiplet-based GPU systems. Yet, previously proposed optical networks lack scalability and cannot be directly applied to existing chiplet-based GPU systems. In this work, we address the challenges of designing large-scale G PU systems with silicon photonic chiplets. We propose GROOT, a group-based optical network that divides the entire system into groups and facilitates resource sharing among the chiplets within each group. Additionally, we design dedicated channel mapping and allocation policies tailored for the request network and the reply network, respectively. Experimental results show that GROOT achieves 48% improvement on performance and 24.5% reduction on system energy consumption over the baseline.