3 citations · 3 across the 12 of their papers we have counts for
12 papers
Self-Correcting Long-Horizon Search Agents via Tree-Structured Memory
Aijun Yang, Qianxue Guo, Ziyi Huang +3
Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments. However, providing complete execution trajectories to th…
WaveTune: Wave-aware Bilinear Modeling for Efficient GPU Kernel Auto-tuning
Kaixuan Zhang, Chutong Ding, Shiyou Qian +6
The rapid adoption of Large Language Models (LLMs) has made GPU inference efficiency an increasingly critical system concern. The runtime of LLM workloads is largely dominated by t…
Hestia: Hyperthread-Level Scheduling for Cloud Microservices with Interference-Aware Attention
Dingyu Yang, Fanyong Kong, Jie Dai +5
Modern cloud servers routinely co-locate multiple latency-sensitive microservice instances to improve resource efficiency. However, the diversity of microservice behaviors, coupled…
TaNG: Modeling Packet Classification with TSS-assisted Neural Networks on GPUs
Zhengyu Liao, Shiyou Qian
Packet classification is a core function in software-defined networks, and learning-based methods have recently shown significant throughput gains on large-scale rulesets. However,…
PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction
Kaixuan Zhang, Yunfan Cui, Shuhao Zhang +8
The rapid expansion of Transformer-based large language models has dramatically increased the need for high-performance GPUs. As a result, there is growing demand for fast, accurat…
LLM-SrcLog: Towards Proactive and Unified Log Template Extraction via Large Language Models
Jiaqi Sun, Wei Li, Heng Zhang +4
Log parsing transforms raw logs into structured templates containing constants and variables. It underpins anomaly detection, failure diagnosis, and other AIOps tasks. Current pars…
PAST: A Primary-Auxiliary Spatio-Temporal Network for Traffic Time Series Imputation
Hanwen Hu, Zimo Wen, Shiyou Qian +1
Traffic time series imputation is crucial for the safety and reliability of intelligent transportation systems, while diverse types of missing data, including random, fiber, and bl…
EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training
Qingao Yi, Jiaang Duan, Jun Zhang +11
Training large language models (LLMs) at scale incurs substantial communication overhead, while static gradient compression cannot adapt to gradient evolution and may degrade model…
GFS: A Preemption-aware Scheduling Framework for GPU Clusters with Predictive Spot Instance Management
Jiaang Duan, Shenglin Xu, Shiyou Qian +15
The surge in large language models (LLMs) has fundamentally reshaped the landscape of GPU usage patterns, creating an urgent need for more efficient management strategies. While cl…
Atys: An Efficient Profiling Framework for Identifying Hotspot Functions in Large-scale Cloud Microservices
Jiaqi Sun, Dingyu Yang, Shiyou Qian +2
To handle the high volume of requests, large-scale services are comprised of thousands of instances deployed in clouds. These services utilize diverse programming languages and are…
LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing
Jiaqi Sun, Shiyou Qian, Zhangchi Han +5
Knowledge Graphs (KGs) structure real-world entities and their relationships into triples, enhancing machine reasoning for various tasks. While domain-specific KGs offer substantia…
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
Yang Gu, Hengyu You, Jian Cao +3
Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificia…