718 citations · 786 across the 4 of their papers we have counts for
4 papers
Stateful Large Language Model Serving with Pensieve
Lingfan Yu, Jinkun Lin, Jinyang Li
Large Language Models (LLMs) are wildly popular today and it is important to serve them efficiently. Existing LLM serving systems are stateless across requests. Consequently, when…
Scalable Graph Neural Networks for Heterogeneous Graphs
Lingfan Yu, Jiajun Shen, Jinyang Li +1
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature s…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…
The Efficient Server Audit Problem, Deduplicated Re-execution, and the Web
Cheng Tan, Lingfan Yu, Joshua B. Leners +1
You put a program on a concurrent server, but you don't trust the server; later, you get a trace of the actual requests that the server received from its clients and the responses…