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        • 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
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        • Interference-aware multiplexing for deep learning in GPU clusters: A middleware approach
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        • UGache: A unified GPU cache for embedding-based deep learning
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        • 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
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      • 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
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        • ESCHER: Expressive scheduling with ephemeral resources
        • Serving unseen deep learning model with near-optimal configurations: A fast adaptive search approach
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        • 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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  1. Reading Notes
  2. Conference

ASPLOS 2024

Last updated 9 months ago

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Papers

LLM Inference

  • SpotServe: Serving Generative Large Language Models on Preemptible Instances [] [] []

    • CMU & PKU & CUHK

    • Distributed LLM serving system on preemptible/spot instances

    • Techniques

      • Dynamically adapt the LLM parallelization configuration

      • Minimize the cost of migrating instances for dynamic re-parallelization

      • Stateful inference recovery

Model Serving

  • Proteus: A High-Throughput Inference-Serving System with Accuracy Scaling []

    • UMass-Amherst & Nokia Bell Labs

Elastic Training

  • Heet: Accelerating Elastic Training in Heterogeneous Deep Learning Clusters

    • UMacau

https://asplos-conference.org/2024/
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