6 citations · 14 across the 9 of their papers we have counts for
5 papers · 1 filter
Phantora: Maximizing Code Reuse in Simulation-based Machine Learning System Performance Estimation
Jianxing Qin, Jingrong Chen, Xinhao Kong +8
Modern machine learning (ML) training workloads place substantial demands on both computational and communication resources. Consequently, accurate performance estimation has becom…
HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
Yongji Wu, Xueshen Liu, Shuowei Jin +6
The Mixture-of-Experts (MoE) architecture has become increasingly popular as a method to scale up large language models (LLMs). To save costs, heterogeneity-aware training solution…
Lazarus: Resilient and Elastic Training of Mixture-of-Experts Models
Yongji Wu, Wenjie Qu, Xueshen Liu +10
Sparsely-activated Mixture-of-Experts (MoE) architecture has increasingly been adopted to further scale large language models (LLMs). However, frequent failures still pose signific…
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
Yao Lu, Song Bian, Lequn Chen +19
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in t…
Punica: Multi-Tenant LoRA Serving
Lequn Chen, Zihao Ye, Yongji Wu +3
Low-rank adaptation (LoRA) has become an important and popular method to adapt pre-trained models to specific domains. We present Punica, a system to serve multiple LoRA models in…