3 papers
cs.DC2026
C2C-Explorer: An Exploration Framework for Chip-to-Chip Interconnect Architectures in LLM Cloud Computing Systems
Jiayi Li, Di Wu, Qingxu Li +10
The scaling-up of large language models (LLMs) necessitates computing systems to have multi-processor-chip architectures, elevating the importance of chip-to-chip (C2C) communicati…
cs.DC2026
Multi-DNN Inference of Sparse Models on Edge SoCs
Jiawei Luo, Di Wu, Simon Dobson +1
Modern edge applications increasingly require multi-DNN inference systems to execute tasks on heterogeneous processors, gaining performance from both concurrent execution and from…
cs.DC2025
Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency
Marco Kurzynski, Shaizeen Aga, Di Wu
Training large language models (LLMs) efficiently requires a deep understanding of how modern GPU systems behave under real-world distributed training workloads. While prior work h…