1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning
Jinsun Yoo, ChonLam Lao, Lianjie Cao +4
This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance, without requiring expensive GPU…
MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference
Abhimanyu Rajeshkumar Bambhaniya, Hanjiang Wu, Suvinay Subramanian +8
Modern LLM serving now spans multi-stage pipelines including RAG retrieval and KV cache reuse, each with distinct compute, memory, and latency demands. Inference engines expose a l…
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…
Accelerating Stateful Network Applications with Performance Prediction on SoC SmartNICs
Shaoke Xi, Jiaqi Gao, Mengqi Liu +7
Offloading stateful network functions to multi-threaded SoC SmartNICs promises significant performance and cost benefits. However, realizing this potential is hindered by two funda…
Optimus: Accelerating Large-Scale Multi-Modal LLM Training by Bubble Exploitation
Weiqi Feng, Yangrui Chen, Shaoyu Wang +3
Multimodal large language models (MLLMs) have extended the success of large language models (LLMs) to multiple data types, such as image, text and audio, achieving significant perf…