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    • Systems for ML
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      • Mixture of Experts (MoE)
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      • Diffusion Models
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  • 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
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      • HotNets 2024
      • SC 2024
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      • 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
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  • AI Infrastructure
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  • Operating Systems
  • Tiered Storage
  • Remote Procedure Call (RPC)
  • AI Security
  • Verification
  • Shared Log
  • Unclassified

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  1. Reading Notes
  2. Conference

HotOS 2025

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Homepage:

Paper list:

Papers

AI Infrastructure

  • Good things come in small packages: Should we build AI clusters with Lite-GPUs? []

    • MSR

  • Storage Class Memory is Dead, All Hail Managed-Retention Memory: Rethinking Memory for the AI Era []

    • MSR

    • MRM: Managed-Retention Memory

Resource Management

  • Granular Resource Demand Heterogeneity []

    • USC

    • hiresperf: a granular resource profiler that investigates resource usage at 10-microsecond intervals attributed to each function invocation with a low overhead

Compound AI Systems

    • MIT & Microsoft Azure

Operating Systems

  • Apiary: An OS for the Modern FPGA

    • UW

  • The NIC should be part of the OS

    • ETH

Tiered Storage

  • Tolerate It if You Cannot Reduce It: Handling Latency in Tiered Memory

    • EPFL

    • Columbia & Microsoft

  • Rethinking Tiered Storage: Talk to File Systems, Not Device Drivers

    • UIUC

Remote Procedure Call (RPC)

    • UW

AI Security

    • Harvard & Princeton

Verification

  • Lightweight Hypervisor Verification: Putting the Hardware Burger on a Diet

    • EPFL

  • Can Large Language Models Verify System Software? A Case Study Using FSCQ as a Benchmark

    • Duke

  • Modular, Full-System Verification

    • BlueRock Security

Shared Log

  • Designing a Datacenter-wide Distributed Shared Log

    • UC Berkeley

Unclassified

  • Towards ML System Extensibility

    • UW

  • Serve Programs, Not Prompts

    • Yale

Towards Resource-Efficient Compound AI Systems []

My CXL Pool Obviates Your PCIe Switch []

Rethinking RPC Communication for Microservices-based Applications []

Guillotine: Hypervisors for Isolating Malicious AIs []

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