5 papers
Frontier: Towards Comprehensive and Accurate LLM Inference Simulation
Yicheng Feng, Xin Tan, Yangtao Deng +3
Modern LLM serving is no longer homogeneous or monolithic. Production systems now combine disaggregated execution, complex parallelism, runtime optimizations, and stateful workload…
TSGuard: Automated User-Centric Incident Diagnosis for AI Workloads in the Cloud
Yitao Yang, Yangtao Deng, Yifan Xiong +3
AI workloads incur frequent failures and incidents from the underlying infrastructure. The current incident management workflow follows a provider-centric paradigm, where users rep…
Why Stop at One Error? Benchmarking LLMs as Data Science Code Debuggers for Multi-Hop and Multi-Bug Errors
Zhiyu Yang, Shuo Wang, Yukun Yan +1
LLMs are transforming software development, yet current code generation and code repair benchmarks mainly assess syntactic and functional correctness in simple, single-error cases.…
Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training
Yangtao Deng, Lei Zhang, Qinlong Wang +13
Reliability is essential for ensuring efficiency in LLM training. However, many real-world reliability issues remain difficult to resolve, resulting in wasted resources and degrade…
Minder: Faulty Machine Detection for Large-scale Distributed Model Training
Yangtao Deng, Xiang Shi, Zhuo Jiang +12
Large-scale distributed model training requires simultaneous training on up to thousands of machines. Faulty machine detection is critical when an unexpected fault occurs in a mach…