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.
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