EuroSys 2025

Meta Info

Homepage: https://2025.eurosys.org

Paper list: https://2025.eurosys.org/accepted-papers.html

Acceptance Rate

• Fall: 8.2% (= 30 / 367)

• Spring: 10.5% (= 42 / ?)

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

  • 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

• 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

• SpotHedge: Serving AI Models on Spot Instances

  • UC Berkeley

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