# EuroSys 2025 ## Meta Info Homepage: Paper list: Proceedings: ### Acceptance Rate * Overall: 12.2% (= 85 / 696) * Total: 85 (= 44 + 41) * 11 revised papers from EuroSys'25 Fall * Fall: 8.2% (= 30 / 367) * 14 revised papers from EuroSys'25 Spring * Total: 44 (= 30 + 14) * Spring: 9.7% (= 32 / 329) * 9 revised papers from EuroSys'24 Fall * Total: 41 (= 32 + 9) ## Papers ### Large Language Models (LLMs) * LLM Training * Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization * UofT * MEPipe: Democratizing LLM Training with Memory-Efficient Slice-Level Pipeline Scheduling on Cost-Effective Accelerators * THU & Zhipu AI * LLM Inference * Fast State Restoration in LLM Serving with HCache * THU * Stateful Large Language Model Serving with Pensieve * NYU * CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion * CUHK-Shenzhen & UChicago & Stanford * **Best Paper Award (Spring)** * T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge * USTC & MSRA * DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMs * ETH & MIT * SpInfer: Leveraging Low-Level Sparsity for Efficient Large Language Model Inference on GPUs * HKUST-GZ * **Best Paper Award (Fall)** * LLM Fine-Tuning * HybridFlow: A Flexible and Efficient RLHF Framework * HKU & ByteDance * Mixture-of-Experts (MoEs) * Samoyeds: Accelerating MoE Models with Structured Sparsity Leveraging Sparse Tensor Cores * SJTU ### Distributed Training * JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs * UNIST & Samsung * FlowCheck: Decoupling Checkpointing and Training of Large-Scale Models * SJTU & Alibaba Cloud * Comprehensive Deadlock Prevention for GPU Collective Communication * PKU & OneFlow ### Model Serving * A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro * UIUC & IBM Research * SkyServe: Serving AI Models across Regions and Clouds with Spot Instances \[[Paper](https://dl.acm.org/doi/10.1145/3689031.3717459)] \[[Code](https://github.com/skypilot-org/skypilot)] \[[arXiv](https://arxiv.org/abs/2411.01438)] * UC Berkeley * Manage a mixture of spot and on-demand replicas across regions and clouds. * Improve availability, reduce correlated preemptions, overprovision cheap spot replicas. * Baselines: AWS Auto-scaling Group (ASG), MArk \[ATC'19], AWS spot node pool (AWSSpot), [SpotServe](https://paper.lingyunyang.com/reading-notes/conference/asplos-2024/spotserve) ### Deep Learning Compilation * SpaceFusion: Advanced Deep Learning Operator Fusion via Space-Mapping Graph * SJTU ### Resource Management * Scheduling * Towards VM Rescheduling Optimization Through Deep Reinforcement Learning * UC Merced & UC Berkeley & ByteDance * Eva: Cost-Efficient Cloud-Based Cluster Scheduling * UW-Madison * Serverless Computing * Serverless Cold Starts and Where to Find Them * Huawei * SeBS-Flow: Benchmarking Serverless Cloud Function Workflows * Karlsruhe Institute of Technology & ETH * AlloyStack: A Library Operating System for Serverless Workflow Applications * TJU & THU * GPU Sharing * Improving GPU Sharing Performance through Adaptive Bubbleless Spatial-Temporal Sharing * SJTU & Microsoft & Alibaba * Multiplexing Dynamic Deep Learning Workloads with SLO-awareness in GPU Clusters * University of Macau & SIAT, CAS ## Acronyms * RLHF: Reinforcement Learning from Human Feedback * ML: Machine Learning