6 citations · 14 across the 9 of their papers we have counts for
9 papers
Curator: Efficient Vector Search with Low-Selectivity Filters
Yicheng Jin, Yongji Wu, Wenjun Hu +4
Embedding-based dense retrieval has become the cornerstone of many critical applications, where approximate nearest neighbor search (ANNS) queries are often combined with filters o…
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…
VcLLM: Video Codecs are Secretly Tensor Codecs
Ceyu Xu, Yongji Wu, Xinyu Yang +4
As the parameter size of large language models (LLMs) continues to expand, the need for a large memory footprint and high communication bandwidth have become significant bottleneck…
Plato: Plan to Efficiently Decode for Large Language Model Inference
Shuowei Jin, Xueshen Liu, Yongji Wu +7
Large language models (LLMs) have achieved remarkable success in natural language tasks, but their inference incurs substantial computational and memory overhead. To improve effici…
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…
Curator: Efficient Indexing for Multi-Tenant Vector Databases
Yicheng Jin, Yongji Wu, Wenjun Hu +3
Vector databases have emerged as key enablers for bridging intelligent applications with unstructured data, providing generic search and management support for embedding vectors ex…
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…