SEECHIP: A Scalable and Energy-Efficient Chiplet-based GPU Architecture Using Photonic Links

PROCEEDINGS OF THE 52ND INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING, ICPP 2023(2023)

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Abstract
The continuous increase in GPU performance benefits a wide range of high-performance computing (HPC) applications. Slower growth of transistor density and limited size of chip die are now posing significant challenges to scale GPUs. The chiplet technology provides a potential solution to surpass these limitations. However, the performance of these chiplet-based GPUs is often constrained by the metallic-based interconnects between the chiplets. Emerging technologies such as photonic interconnect can overcome the limitations of metallic interconnects, offering several superior properties, such as high bandwidth density and low energy consumption. In this paper, we propose SEECHIP: a Scalable and Energy-Efficient CHIPlet-based GPU architecture using photonic links. SEECHIP introduces a novel photonic inter-chiplet network that supports both unicast and broadcast communication, providing the same transmission bandwidth at both the sending and receiving ends. In addition, we propose a tailored hierarchical memory architecture, which is more suitable for the parallelization of general-purpose HPC applications. Simulation results using 14 benchmarks show that SEECHIP can achieve 48.4% and 62.7% reduction in execution time and energy consumption, respectively, as compared to other GPUs with metallic or photonic interconnects. Simulation results also show that SEECHIP has good scalability compared with the other GPUs.
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Key words
GPU,Scalabilty,Energy-Efficient,Chiplet,Photonic links
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