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Awesome Papers
  • Introduction
  • Paper List
    • Systems for ML
      • Data Processing
      • Deep Learning Training
      • Resource Scheduler
      • Model Serving
      • Large Language Model (LLM)
      • Diffusion Models
      • Deep Learning Recommendation Model (DLRM)
      • Mixture of Experts (MoE)
      • Hyper-Parameter Tuning (HPO)
      • Reinforcement Learning (RL)
      • Deep Learning Compiler
      • Deep Learning Framework
      • Cloud-Edge Collaboration
    • ML for Systems
    • Artificial Intelligence (AI)
      • Diffusion Models
      • Language Models
      • Deep Learning Recommendation Model (DLRM)
    • Hardware Virtualization
      • GPU Sharing
    • Resource Disaggregation
      • GPU Disaggregation
      • Memory Disaggregation
    • Resource Fragmentation
    • Cloud Computing
      • Sky Computing
      • Serverless Computing
      • Spot Instances
    • Remote Direct Memory Access (RDMA)
    • Research Skills
    • Miscellaneous
  • Reading Notes
    • Conference
      • ICML 2025
      • ATC 2025
      • OSDI 2025
      • HotOS 2025
      • MLSys 2025
      • NSDI 2025
      • ASPLOS 2025
      • EuroSys 2025
      • HPCA 2025
      • PPoPP 2025
      • NeurIPS 2024
      • SoCC 2024
      • HotNets 2024
      • SC 2024
      • SOSP 2024
      • VLDB 2024
      • SIGCOMM 2024
      • ICML 2024
      • ATC 2024
      • OSDI 2024
      • ISCA 2024
      • CVPR 2024
      • MLSys 2024
      • ASPLOS 2024
        • SpotServe: Serving generative large language models on preemptible instances
      • EuroSys 2024
        • Orion: Interference-aware, fine-grained GPU sharing for ML applications
      • NSDI 2024
      • NeurIPS 2023
      • SC 2023
        • Interference-aware multiplexing for deep learning in GPU clusters: A middleware approach
      • SoCC 2023
      • SOSP 2023
        • UGache: A unified GPU cache for embedding-based deep learning
      • SIGCOMM 2023
      • HotChips 2023
      • ICML 2023
      • ATC 2023
        • Accelerating Distributed MoE Training and Inference with Lina
        • SmartMoE: Efficiently Training Sparsely-Activated Models ...
        • Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent
      • OSDI 2023
      • HotOS 2023
      • SIGMOD 2023
      • ISCA 2023
      • MLSys 2023
      • EuroSys 2023
      • NSDI 2023
        • Shepherd: Serving DNNs in the wild
        • Understanding RDMA microarchitecture resources for performance isolation
        • Skyplane: Optimizing transfer cost and throughput using cloud-aware overlays
        • Shockwave: Fair and efficient cluster scheduling for dynamic adaptation in machine learning
      • ASPLOS 2023
        • TPP: Transparent page placement for CXL-enabled tiered-memory
        • EVStore: Storage and caching capabilities for scaling embedding tables in deep recommendation system
        • Lucid: A non-intrusive, scalable and interpretable scheduler for deep learning training jobs
      • SC 2022
      • SoCC 2022
        • ESCHER: Expressive scheduling with ephemeral resources
        • Serving unseen deep learning model with near-optimal configurations: A fast adaptive search approach
      • SIGCOMM 2022
        • Multi-resource interleaving for deep learning training
      • ATC 2022
        • PilotFish: Harvesting Free Cycles of Cloud Gaming with Deep Learning Training
        • Memory Harvesting in Multi-GPU Systems with Hierarchical Unified Virtual Memory
        • Whale: Efficient Giant Model Training over Heterogeneous GPUs
        • DVABatch: Diversity-aware Multi-Entry Multi-Exit Batching for Efficient Processing of DNN Service...
        • Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing
        • SOTER: Guarding Black-box Inference for General Neural Networks at the Edge
        • Direct access, high-performance memory disaggregation with DirectCXL
      • OSDI 2022
        • Orca: A distributed serving system for transformer-based generative models
        • Microsecond-scale preemption for concurrent GPU-accelerated DNN inferences
        • Looking beyond GPUs for DNN scheduling on multi-tenant clusters
      • IPDPS 2022
        • DGSF: Disaggregated GPUs for serverless functions
      • EuroSys 2022
        • Slashing the disaggregation tax in heterogeneous data centers with FractOS
      • NSDI 2022
      • SoCC 2021
      • ATC 2021
        • Zico: Efficient GPU memory sharing for concurrent DNN training
      • OSDI 2021
        • Pollux: Co-adaptive cluster scheduling for goodput-optimized deep learning
      • SOSP 2021
        • HeMem: Scalable Tiered Memory Management for Big Data Applications and Real NVM
      • EuroSys 2021
        • Take it to the limit: Peak prediction-driven resource overcommitment in datacenters
      • HotOS 2021
        • From cloud computing to sky computing
      • NSDI 2021
      • OSDI 2020
        • A unified architecture for accelerating distributed DNN training in heterogeneous GPU/CPU clusters
        • HiveD: Sharing a GPU cluster for deep learning with guarantees
      • ATC 2020
        • Serverless in the wild: Characterizing and optimizing the serverless workload
      • EuroSys 2020
      • ASPLOS 2020
      • MLSys 2020
      • SoCC 2020
        • Elastic Parameter Server Load Distribution in Deep Learning Clusters
      • HPDC 2020
        • KubeShare: A framework to manage GPUs as first-class and shared resources in container cloud
      • CLUSTER 2019
      • EuroSys 2019
      • NSDI 2019
      • IWQoS 2019
        • Who limits the resource efficiency of my datacenter: An analysis of Alibaba datacenter traces
      • SIGCOMM 2018
        • Revisiting network support for RDMA
      • OSDI 2018
        • Ray: A distributed framework for emerging AI applications
      • EuroSys 2018
        • Medea: Scheduling of long running applications in shared production clusters
      • ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018
        • GaiaGPU: Sharing GPUs in container clouds
      • SoCC 2017
        • SLAQ: Quality-driven scheduling for distributed machine learning
      • ASPLOS 2017
        • Neurosurgeon: Collaborative intelligence between the cloud and mobile edge
      • NSDI 2017
        • Clipper: A low-latency online prediction serving system
      • CLUSTER 2014
        • Evaluating job packing in warehouse-scale computing
    • Journal
      • IEEE Transactions on Cloud Computing
        • 2021
          • Gemini: Enabling multi-tenant GPU sharing based on kernel burst estimation
      • ACM Computing Surveys
        • 2017
          • GPU virtualization and scheduling methods: A comprehensive survey
      • ACM SIGCOMM Computer Communication Review (CCR)
        • 2021
          • Data-driven Networking Research: models for academic collaboration with industry
        • 2007
          • How to Read a Paper
      • Communications of the ACM
        • 2015
          • Why Google stores billions of lines of code in a single repository
    • Miscellaneous
      • arXiv
        • 2024
          • Efficiently programming large language models using SGLang
        • 2023
          • HexGen: Generative inference of foundation model over heterogeneous decentralized environment
          • High-throughput generative inference of large language models with a single GPU
        • 2022
          • DisaggRec: Architecting disaggregated systems for large-scale personalized recommendation
          • A case for disaggregation of ML data processing
          • Singularity: Planet-scale, preemptive and elastic scheduling of AI workloads
          • Aryl: An elastic cluster scheduler for deep learning
        • 2016
          • Wide & deep learning for recommender systems
          • Training deep nets with sublinear memory cost
      • MSR Technical Report
        • 2011
          • Heuristics for vector bin packing
  • About Myself
    • Academic Profile
    • Personal Blog (in Chinese)
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  • Acceptance Rate
  • Papers
  • Large Language Models (LLMs)
  • Deep Learning Recommendation Models (DLRMs)
  • Model Serving
  • Collective Communication
  • Networking
  • Resource Management
  • Fault Tolerance
  • Memory Disaggregation
  • Real-Time Video Streaming

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  1. Reading Notes
  2. Conference

NSDI 2025

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Meta Info

Homepage:

Paper list:

Acceptance Rate

  • Total: 12.5% (= 83 / 666)

  • Fall: 13.7% (= 55 / 401)

  • Spring: 10.6% (= 28 / 265)

Papers

Large Language Models (LLMs)

  • LLM Training

    • Minder: Faulty Machine Detection for Large-scale Distributed Model Training []

      • THU & ByteDance & NEU & Harvard

      • Automatically and efficiently detect faulty distinctive monitoring metric patterns.

    • Holmes: Localizing Irregularities in LLM Training with Mega-scale GPU Clusters []

      • FDU & Tencent & UChicago

    • Evolution of Aegis: Fault Diagnosis for AI Model Training Cloud Service in Production []

      • Alibaba Cloud

    • Accelerating Design Space Exploration for LLM Training Systems with Multi-experiment Parallel Simulation []

      • THU & Zhongguancun Lab & UPenn

    • SimAI: Unifying Architecture Design and Performance Tunning for Large-Scale Large Language Model Training with Scalability and Precision []

      • Alibaba Cloud

  • Reinforcement Learning with Human Feedback (RLHF)

    • Optimizing RLHF Training for Large Language Models with Stage Fusion [] []

      • PKU & StepFun

  • Checkpointing

    • BCP: A Unified Checkpointing System for Large Foundation Model Development []

      • HKU & ByteDance

Deep Learning Recommendation Models (DLRMs)

    • HKUST & Alibaba

Model Serving

    • GaTech & UC Berkeley & Adobe

Collective Communication

    • USTC & Microsoft

    • Purdue & NVIDIA & VMware Research & Feldera

    • UW & Raytheon BBN Technologies & MIT

Networking

  • Remote Direct Memory Access (RDMA)

      • UW & UW-Madison

      • Alibaba Cloud

  • Application Networks

      • UW & Duke

  • Container Overlay Network

      • SJTU & Broadcom

  • Placement

      • Google & USC & Harvard & UCLA & Columbia

  • Network Mitigation

      • USC & Microsoft

Resource Management

  • Granular Management

      • MIT & Brown & USC & VMware Research

        • Provide developers with familiar, high-level abstractions (e.g., data structures, batch computing); decompose them into resource proclets, granular units that each primarily consume resources of one type; split, merge, and migrate resource proclets in milliseconds.

      • ICL

  • Resource Scheduling

      • HKUST

  • Serverless Computing

      • UW-Madison

  • Userspace Scheduling

      • UCSD

Fault Tolerance

    • UMich & SJTU

Memory Disaggregation

    • UCAS & PKU & Huawei Cloud & SJTU

    • UCSD & Technion & VMware Research

Real-Time Video Streaming

    • Princeton

GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale []

SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads []

AutoCCL: Automated Collective Communication Tuning for Accelerating Distributed and Parallel DNN Training []

OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud []

Efficient Direct-Connect Topologies for Collective Communications []

White-Boxing RDMA with Packet-Granular Software Control []

Mitigating Scalability Walls of RDMA-based Container Networks []

High-level Programming for Application Networks []

ONCache: A Cache-Based Low-Overhead Container Overlay Network []

Preventing Network Bottlenecks: Accelerating Datacenter Services with Hotspot-Aware Placement for Compute and Storage []

Enhancing Network Failure Mitigation with Performance-Aware Ranking []

Quicksand: Harnessing Stranded Datacenter Resources with Granular Computing []

GRANNY: Granular Management of Compute-Intensive Applications in the Cloud []

GREEN: Carbon-efficient Resource Scheduling for Machine Learning Clusters []

Making Serverless Pay-For-Use a Reality with Leopard []

The Benefits and Limitations of User Interrupts for Preemptive Userspace Scheduling []

One-Size-Fits-None: Understanding and Enhancing Slow Fault Tolerance in Modern Distributed Systems []

Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs []

Eden: Developer-Friendly Application-Integrated Far Memory []

Mowgli: A Passive Approach to Learning Real-Time Rate Control for Video Conferencing []

https://www.usenix.org/conference/nsdi25
https://www.usenix.org/conference/nsdi25/technical-sessions
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