2 citations · 3 across the 12 of their papers we have counts for
9 papers · 1 filter
KREX: Concurrent Kernel Benchmarking on Shared GPUs via Region-Granular Exclusivity
Tianyu Feng, Haoxuan Yu, Tianyuan Wu +8
LLM agents automate GPU kernel optimization by repeatedly composing candidates and measuring their duration on real GPUs. Existing systems preserve measurement fidelity by reservin…
ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform
Xiaoxiao Jiang, Suyi Li, Sheng Yao +5
Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them intermittently. Existing platforms…
Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs
Zhihao Xu, Hao Zhong, Zeting Zhou +9
This paper aims to enable computation- and communication-efficient GPU sharing across devices within local area networks (LANs), facilitating ubiquitous AI inference on heterogeneo…
DisagFusion: Asynchronous Pipeline Parallelism and Elastic Scheduling for Disaggregated Diffusion Serving
Hantian Zha, Teng Ma, Yang Yong +7
Diffusion-based generation is increasingly powering production content pipelines; however, deploying these models at scale remains a significant challenge. Model weights frequently…
InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling
Xiaoxiao Jiang, Suyi Li, Lingyun Yang +12
Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image editing typically involves a mask…
Adaptra: Straggler-Resilient Hybrid-Parallel Training with Pipeline Adaptation
Tianyuan Wu, Lunxi Cao, Hanfeng Lu +8
Training large Deep Neural Network (DNN) models at scale often encounters straggler issues, mostly in communications due to network congestion, RNIC/switch defects, or topological…
FALCON: Pinpointing and Mitigating Stragglers for Large-Scale Hybrid-Parallel Training
Tianyuan Wu, Wei Wang, Yinghao Yu +7
Fail-slows, or stragglers, are common but largely unheeded problems in large-scale hybrid-parallel training that spans thousands of GPU servers and runs for weeks to months. Yet, t…
SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules
Suyi Li, Lingyun Yang, Xiaoxiao Jiang +12
Text-to-image (T2I) generation using diffusion models has become a blockbuster service in today's AI cloud. A production T2I service typically involves a serving workflow where a b…
CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference
Suyi Li, Hanfeng Lu, Tianyuan Wu +6
Pre-trained large language models (LLMs) often need specialization for domain-specific tasks. Low-Rank Adaptation (LoRA) is a popular approach that adapts a base model to multiple…