5 papers
DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale
Jialiang Huang, Hongxuan Tang, Jingchang Chen +128
Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools,…
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…
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…
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…
Lit Silicon: A Case Where Thermal Imbalance Couples Concurrent Execution in Multiple GPUs
Marco Kurzynski, Shaizeen Aga, Di Wu
GPU systems are increasingly powering modern datacenters at scale. Despite being highly performant, GPU systems can exhibit performance variation at the node and cluster levels. Su…