5 citations · 6 across the 12 of their papers we have counts for
6 papers · 1 filter
A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM
Shaoke Xi, ChonLam Lao, Boyi Jia +11
Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debugging, and performance-tuning…
Simple is Better: Multiplication May Be All You Need for LLM Request Scheduling
Dingyan Zhang, Jinbo Han, Kaixi Zhang +6
High-quality LLM request scheduling requires meeting two key objectives: ensuring the routed instance has KVCache to accelerate request execution, and ensuring that the workload is…
ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
Yuxing Xiang, Xue Li, Kun Qian +3
With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Pra…
A Survey of Distributed Graph Algorithms on Massive Graphs
Lingkai Meng, Yu Shao, Long Yuan +7
Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and…
Unicron: Economizing Self-Healing LLM Training at Scale
Tao He, Xue Li, Zhibin Wang +4
Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current f…
GraphScope Flex: LEGO-like Graph Computing Stack
Tao He, Shuxian Hu, Longbin Lai +18
Graph computing has become increasingly crucial in processing large-scale graph data, with numerous systems developed for this purpose. Two years ago, we introduced GraphScope as a…