# 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](/reading-notes/conference/asplos-2024/spotserve.md) ### 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://paper.lingyunyang.com/reading-notes/conference/eurosys-2025.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.