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SIGCOMM 2024

Meta Info

Homepage: https://conferences.sigcomm.org/sigcomm/2024/

Paper list

Papers

Large Language Models (LLMs)

  • Systems/Networking for LLM
    • CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving [Paper] [arXiv] [Code] [Video]
      • UChicago & Microsoft & Stanford
      • CacheGen: A context-loading module for LLM systems.
        • Use a custom tensor encoder to encode a KV cache into more compact bitstream representations with negligible decoding overhead.
        • Adapt the compression level of different parts of a KV cache to cope with changes in available bandwidth.
      • Objective: Focus on reducing the network delay in fetching the KV cache → TTFT reduction.
    • Alibaba HPN: A Data Center Network for Large Language Model Training [Paper] [Video]
      • Alibaba Cloud
      • Experience Track
      • LLM training's characteristics
        • Produce a small number of periodic, bursty flows (e.g., 400Gbps) on each host.
        • Require GPUs to complete iterations in synchronization; more sensitive to single-point failure.
      • Alibaba High-Performance Network (HPN): Introduce a 2-tier, dual-plane architecture capable of interconnecting 15K GPUs within one Pod.
        • Benefits: eliminate hash polarization; simplify the optimal path selections.
    • RDMA over Ethernet for Distributed Training at Meta Scale [Paper] [Blog]
      • Meta
      • Experience Track
      • Deploy a combination of centralized traffic engineering and an Enhanced ECMP (Equal-Cost Multi-Path) scheme to achieve optimal load distribution for training workloads.
      • Design a receiver-driven traffic admission via the collective library -> Co-tune both the collective library configuration and the underlying network configuration.
  • LLMs for Networking
    • NetLLM: Adapting Large Language Models for Networking [Paper]
      • CUHK-Shenzhen & Tsinghua SIGS & UChicago
      • NetLLM: Empower the LLM to process multimodal data in networking and generate task-specific answers.
      • Study three networking-related use cases: viewport prediction, adaptive bitrate streaming, and cluster job scheduling.

Distributed Training

  • Crux: GPU-Efficient Communication Scheduling for Deep Learning Training [Paper] [Dataset]
    • Alibaba Cloud
    • Observation: Communication contention among different deep learning training (DLT) jobs seriously influences the overall GPU computation utilization -> Low efficiency of the training cluster.
    • Crux: A communication scheduler
      • Objective: Mitigate the communication contention among DLT jobs -> Maximize GPU computation utilization.
      • Designs: reduce the GPU utilization problem to a flow optimization problem; GPU intensity-aware communication scheduling; prioritize the DLT flows with high GPU computation intensity.
  • Accelerating Model Training in Multi-cluster Environments with Consumer-grade GPUs [Paper]
    • KAIST & UC Irvine & VMware Research
    • StellaTrain: Cache-aware gradient compression; a CPU-based sparse optimizer.
    • Adapt training configurations to fluctuating dynamic network bandwidth -> Enable co-training using on-premises and cloud clusters.

Data Processing

  • Turbo: Efficient Communication Framework for Large-scale Data Processing Cluster [Paper]
    • Tencent & FDU & NVIDIA & THU
    • Experience Track
    • Network throughput & scalability: A dynamic block-level flowlet transmission mechanism; a non-blocking communication middleware.
    • System reliability: Utilize an external shuffle service as well as TCP serving as a backup.
    • Integrated into Apache Spark.

Data Transfers

  • An exabyte a day: Throughput-oriented, Large-scale, Managed Data Transfers with Effingo [Paper]
    • Google
    • Experience Track
    • Effingo: A copy system, integrated with resource management and authorization systems.
      • Per-cluster deployments -> Limit failure domains to individual clusters.
      • Separation from the bandwidth management layer (BwE) -> A modular design that reduces dependencies.