1 citations · 1 across the 5 of their papers we have counts for
7 papers
Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning
Jiaxing Qi, Zhongzhi Luan, Hongyu Zhang +5
Large language models (LLMs) are increasingly used to interpret operational evidence and assist incident response in cloud-native microservice systems. However, recovery-oriented u…
FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning
Yao Lu, Jiaxing QI, Zhongzhi Luan +4
Large language models (LLMs) have emerged as important components across various fields, yet their training requires substantial computation resources and abundant labeled data. It…
RATrain: A Resource-Aware Training Runtime for Large Language Models on Bandwidth-Constrained Heterogeneous Supercomputing Platforms
Yao Lu, Shiqing Ma, Zhongzhi Luan +5
Production heterogeneous supercomputing platforms are increasingly used to host large language model (LLM) training workloads. However, existing GPU-oriented training runtimes typi…
Transformer-based Neural Operators for 3D Wind Field Prediction over Complex Mountainous Terrain
Yujia Zhang, Jiaxi Qi, Ruiyan Chen +5
Accurate prediction of three-dimensional (3D) wind fields over complex mountainous terrain is essential for renewable energy deployment and regional weather modeling. Traditional c…
Bandwidth-Aware LLM Inference on Heterogeneous Many-Core Supercomputers
Yao Lu, Zhongzhi Luan, Gen Li +6
Large language model (LLM) inference is limited by high computational cost and memory bandwidth demands, making deployment on heterogeneous many-core processors challenging. Taking…
Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection
Jiaxing Qi, Chang Zeng, Zhongzhi Luan +5
Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often s…