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.

Last updated 4 months ago