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For the complete documentation index, see llms.txt. This page is also available as Markdown.

EuroSys 2026

Meta Info

Homepage: https://2026.eurosys.org

Paper list: https://2026.eurosys.org/papers.html

Acceptance Rate

  • Spring: 19.6% (= 79 / 404)

Papers

Large Language Models (LLMs)

  • LLM Training

    • MoE Training

      • MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production [Paper] [arXiv]

        • PKU & ByteDance

        • Present MegaScale-MoE, a production system for efficient large-scale MoE training.

        • Co-design communication-efficient parallelism, inter- and intra-operator communication-computation overlap, and lower-precision communication compression for MoE layers.

    • LoRA Fine-Tuning

      • LoRAFusion: Efficient LoRA Fine-Tuning for LLMs [Paper] [arXiv]

        • UofT & Vector Institute & NVIDIA

        • Present LoRAFusion, a system that improves LoRA fine-tuning by optimizing both fused kernels and multi-job training schedules.

        • Combine graph-splitting-based kernel fusion with multi-job adaptive batching to reduce memory traffic, improve communication overlap, and mitigate pipeline bubbles.

      • Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices [Paper]

        • Shandong University & XJTU

        • Study federated fine-tuning for sparsely activated LLMs under resource-constrained client devices.

    • Data Pipeline

      • MegaScale-Data: Scaling DataLoader for Multisource Large Foundation Model Training [Paper] [arXiv]

        • HKU & ByteDance

        • Present MegaScale-Data, an industrial-grade distributed data loading architecture for multi-source large foundation model training.

        • Disaggregate preprocessing with role-specific actors and use a centralized declarative data plane to support scalable multi-source orchestration under heterogeneous preprocessing costs.

    • Scheduling and Parallelism

      • STAlloc: Enhancing Memory Efficiency in Large-Scale Model Training with Spatio-Temporal Planning [Paper]

        • THU & Infinigence-AI & SJTU

        • Plans memory usage across spatial and temporal dimensions to improve large-scale model training efficiency.

      • Zeppelin: Balancing Variable-length Workloads in Data Parallel Large Model Training [Paper]

        • PKU & ETH & CUHK & Shanghai AI Lab & MIT

        • Balances variable-length data-parallel training workloads to reduce stragglers and improve utilization.

      • Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design [Paper]

        • SJTU & Lenovo Research & Microsoft & Guizhou University & NUS

        • Co-designs dynamic scheduling and adaptive parallelism for efficient large-model training.

      • HARP: Orchestrating Automated Parallel Training on Heterogeneous GPU Clusters [Paper]

        • Fudan & Shandong Computer Science Center

        • Automates parallel training orchestration across heterogeneous GPU clusters.

      • HetAuto: Cross-Cluster Auto-Parallelism for Heterogeneous Distributed Training [Paper]

        • HKU & Meituan

        • Searches cross-cluster auto-parallel strategies for heterogeneous distributed training environments.

      • Efficient and Adaptable Overlapping for Computation and Communication via Signaling and Reordering [Paper]

        • THU & PKU & Infinigence-AI & SJTU

        • Uses signaling and reordering to adaptively overlap computation and communication.

      • Crimson: Collaborative Parameter Updates for Efficient Pipeline Training of Large Language Models [Paper]

        • SYSU & HKUST & Pengcheng Laboratory

        • Coordinates parameter updates to improve pipeline training efficiency for large language models.

      • Suika: Efficient and High-quality Re-scheduling of 3D-parallelized LLM Training Jobs in Shared Clusters [Paper]

        • SJTU & TeleAI & Huawei

        • Re-schedules 3D-parallel LLM training jobs in shared clusters while preserving training quality.

    • Runtime Modeling

      • Maya: Optimizing Deep Learning Training Workloads using GPU Runtime Emulation [Paper] [arXiv]

        • Georgia Tech & NVIDIA

        • Present Maya, a performance modeling system for deep learning training based on transparent GPU device emulation.

        • Intercept device API calls from unmodified training code to observe low-level operations without workload translation or code modification.

    • Multimodal Training

      • MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production [Paper] [arXiv]

        • SJTU & ByteDance

        • Present MegaScale-Omni, a production multimodal LLM training system for dynamic modality mixtures and sample-length distributions.

        • Combine decoupled encoder/backbone parallelism, encoder-LLM joint pipelines, and workload balancing for large-scale MLLM training.

    • Fault Tolerance

      • Handling Network Faults in Distributed AI Training: Failover is Now an Option [Paper]

        • NUS & ByteDance

        • Revisit network-fault handling in distributed AI training and make failover practical for large jobs.

  • LLM Inference

    • Speculative Decoding

      • AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding [Paper]

        • CMU & Princeton & EPFL & AWS & Purdue

        • Customize speculative decoding policies for serving workloads with multiple latency SLOs.

    • Request Scheduling

      • FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters [Paper] [arXiv]

        • SIAT, CAS & UCAS & UCSD & University of Macau

        • Refactors in-flight pipelines to adapt LLM serving to fragmented serverless GPU clusters.

      • TokenFlow: Responsive LLM Text Streaming Serving under Request Burst via Preemptive Scheduling [Paper]

        • SJTU & GMU & China Telecom Shanghai

        • Applies preemptive scheduling to keep text-streaming LLM serving responsive under request bursts.

      • AdaGen: Workload-Adaptive Cluster Scheduler for Latency-Optimal LLM Inference Serving [Paper]

        • UVA & HPE Labs & UC Riverside

        • Adapts cluster scheduling decisions to workload shifts for latency-optimized LLM inference.

      • SkyWalker: A Locality-Aware Cross-Region Load Balancer for LLM Inference [Paper]

        • UC Berkeley & RUC & Rice

        • Uses locality-aware cross-region load balancing for distributed LLM inference services.

      • PiLLM: Resource-Efficient LLM Inference Using Workload Prediction [Paper]

        • ShanghaiTech & SenseTime & BUAA

        • Predicts workload behavior to improve resource efficiency in LLM inference.

    • KV Cache and Memory Management

      • Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading [Paper] [arXiv]

        • Stevens Institute of Technology & Waterloo & Rutgers

        • Uses fine-grained expert offloading to balance latency and memory pressure in MoE-based LLM serving.

      • KUNSERVE: Parameter-centric Memory Management for Efficient Memory Overloading Handling in LLM Serving [Paper]

        • SJTU

        • Manages model parameters as the central memory object to handle memory overloads during LLM serving.

      • High Throughput and Low Latency LLM Serving via Adaptive KV Caching [Paper]

        • University of Macau & SIAT, CAS & NTU

        • Adapts KV caching decisions to jointly improve throughput and latency.

    • Multiplexing

      • MFS: An Efficient Model Family Serving System for LLMs [Paper]

        • HKUST & USTC & Inspur

        • Serves related LLM model families by exploiting shared model structure and deployment opportunities.

      • Efficient Multimodal Serving via Module Multiplexing [Paper]

        • HKUST & SYSU & XJTU & MetaX

        • Multiplexes reusable multimodal model modules to improve serving efficiency.

    • Sparsity

      • SAS: Sparse Attention Synthesizer for Efficient Language Model Inference [Paper]

        • Amazon

        • Synthesizes sparse attention patterns to accelerate language-model inference.

    • Heterogeneous Deployment

      • Scaling LLM Test-Time Compute with Mobile NPU on Smartphones [Paper]

        • THU & USTC & MSR & AIR, THU

        • Explores using mobile NPUs to scale LLM test-time compute on smartphones.

      • TailorLLM: Collaborative End-Cloud Inference of Large and Small Language Models Based on Low-Rank Adaptation [Paper]

        • BUPT

        • Coordinates cloud and edge execution between large and small language models using low-rank adaptation.

    • Trusted Execution

      • TZ-LLM: Protecting On-Device Large Language Models with Arm TrustZone [Paper]

        • SJTU

        • Protects on-device LLM execution with Arm TrustZone-based isolation.

    • LLM-based Applications

      • AIMS: Cost-Efficient LLM-Based Agent Deployment in Hybrid Cloud-Edge Environments [Paper]

        • UVA & Microsoft

        • Places and executes LLM-based agents across hybrid cloud-edge environments with cost efficiency.

      • From Imperative to Declarative: Towards LLM-friendly OS Interfaces for Boosted Computer-Use Agents [Paper]

        • IS, CAS & UCAS & SJTU

        • Recasts OS interfaces from imperative actions toward declarative forms that are easier for computer-use agents to operate.

Diffusion Models

  • Image Editing

    • FlashPS: Efficient Generative Image Editing with Mask-aware Caching and Scheduling [Paper] [arXiv] [Code]

      • HKUST & Alibaba

      • Our work!

      • Uses mask-aware caching and scheduling to accelerate generative image editing workloads.

Model Serving

  • Automated End-to-End Model Serving with Cooperative Compilation and Scheduling [Paper]

    • NJU & Hunan University

    • Jointly optimizes model-serving compilation and scheduling decisions across the end-to-end serving stack.

Resource Management

  • Serverless Computing

    • Efficient Data Passing for Serverless Inference Workflows: A GPU-Centric Approach [Paper]

      • HUST & CUHK-Shenzhen & TeleAI & HKUST

      • Designs GPU-centric data passing for serverless inference workflows to reduce transfer overhead.

    • iRoute: Local Routing Table-based Workflow Management in Serverless Computing [Paper]

      • TJU & THU & IEIT Systems & Inspur

      • Uses local routing-table mechanisms to manage serverless workflows.

    • DROPS: Managing Serverless Resource Pools in Microsoft Azure Functions [Paper]

      • Waterloo & MSR & Microsoft

      • Manages serverless resource pools in Azure Functions to improve allocation efficiency.

    • Squeezy: Rapid VM Memory Reclamation for Serverless Functions [Paper]

      • NTUA & UIUC

      • Reclaims VM memory quickly for serverless functions to improve density and responsiveness.

    • Demystifying Serverless Costs on Public Platforms: Bridging Billing, Architecture, and OS Scheduling [Paper]

      • UBC & Johns Hopkins

      • Connects public-platform billing behavior with architecture and OS scheduling effects in serverless systems.

    • Fix: externalizing network I/O in serverless computing [Paper]

      • Stanford

      • Externalizes network I/O from serverless functions to reduce runtime overhead and simplify function execution.

  • GPU Cluster Management

    • Bridging the GPU Utilization Gap: Predictive Multi-Dimensional Resource Scheduling for AI Workloads [Paper]

      • THU & Alibaba & SJTU

      • Uses predictive multidimensional scheduling to improve GPU utilization for AI workloads.

    • Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings [Paper] [PDF]

      • ETS & Inria & OVHcloud & CNRS

      • Present practical job-level power estimation methods for GPUs under temporal sharing, spatial sharing, and passthrough deployment modes in cloud environments.

      • Show that GPU sharing can improve energy efficiency for small AI workloads, and identify substantial GPU underutilization in an IaaS GPU cluster.

Acronyms

  • LLM: Large Language Model

  • LoRA: Low-Rank Adaptation

  • MoE: Mixture-of-Experts

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