ML Networking
Optimizing network infrastructure for distributed machine learning training and inference at massive scale.
Overview
ML networking focuses on building and optimizing network infrastructure for distributed machine learning at scale. Training large models requires synchronizing gradients across thousands of accelerators, creating enormous network traffic. Inference serving requires low-latency communication between model components. Network performance often becomes the bottleneck in distributed systems. This field combines networking expertise with understanding of ML communication patterns to build efficient infrastructure.
Key Research Areas
High-bandwidth interconnects for training
Gradient compression and communication
Network topology design for ML
Collective communication optimization
In-network computation
Fault tolerance in distributed training
Research Challenges
Communication overhead limiting scalability
Network congestion in large clusters
Long-tail latency in distributed systems
Stragglers slowing down training
Cost of high-performance networking
Compatibility with diverse hardware
Practical Applications
Training models across thousands of GPUs
Distributed inference for large models
Federated learning across devices
Multi-datacenter training
Low-latency serving infrastructure
Research clusters for academic institutions
Technical Deep Dive
ML workloads have specific communication patterns that can be optimized. All-reduce operations for gradient synchronization benefit from ring or tree topologies. Modern clusters use high-bandwidth interconnects like InfiniBand or custom fabrics (NVLink, TPU interconnect). Gradient compression techniques reduce communication volume at the cost of some convergence slowdown. Collective communication libraries (NCCL, Gloo) implement efficient algorithms for multi-GPU primitives. Network topology matters—full bisection bandwidth is ideal but expensive. In-network aggregation using programmable switches can reduce traffic. Handling failures without losing progress requires checkpointing and fault-tolerant training protocols.
Future Research Directions
Future ML networking will need to scale to even larger clusters as models continue growing. Optical interconnects may provide bandwidth and energy advantages. In-network computation could offload more ML operations to networking hardware. Better scheduling and routing algorithms that understand ML traffic patterns will improve efficiency. As training becomes more distributed geographically, wide-area networking for ML becomes important. Automated network configuration that adapts to workload characteristics will reduce manual tuning.
Related Research Topics
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