5 papers
Evaluating Agentic Code Repair Capabilities in Distributed Systems
Yibo Yan, Huijuan Wang, Junzhou He +4
LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified. Distributed-system debugging,…
Moebius: Serving Mixture-of-Expert Models with Seamless Runtime Parallelism Switch
Shaoyu Wang, Yizhuo Liang, Jaeyong Song +2
Mixture-of-Experts (MoE) architectures scale large language models (LLMs) to hundreds of billions of parameters. Serving a single MoE model requires multiple GPUs operating in para…
Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
Shaoyu Wang, Guangrong He, Geon-Woo Kim +2
Mixture-of-Experts (MoE) architectures offer the promise of larger model capacity without the prohibitive costs of fully dense designs. However, in real-world inference serving, lo…
Optimus: Accelerating Large-Scale Multi-Modal LLM Training by Bubble Exploitation
Weiqi Feng, Yangrui Chen, Shaoyu Wang +3
Multimodal large language models (MLLMs) have extended the success of large language models (LLMs) to multiple data types, such as image, text and audio, achieving significant perf…
StarTrail: Concentric Ring Sequence Parallelism for Efficient Near-Infinite-Context Transformer Model Training
Ziming Liu, Shaoyu Wang, Shenggan Cheng +5
Training Transformer models on long sequences in a distributed setting poses significant challenges in terms of efficiency and scalability. Current methods are either constrained b…