Distributed Training
A technical paradigm that distributes deep learning model training tasks across multiple GPUs or nodes for parallel execution. It accelerates large-scale model training through strategies such as data parallelism and model parallelism, relying on high-speed interconnect networks for gradient synchronization.
Timeline (last 90 days)
In large-scale distributed training, forward propagation, backward propagation, and AllReduce/AllGather communication operations are interleaved; if a communication operation is not complete, downstream compute operators must wait, creating bubbles.
Communication primitives in distributed training (such as AllReduce gradient aggregation) can introduce non-deterministic accumulated errors due to differences in parallelism configuration and network topology.
Hidden failures such as network topology asymmetry, collective communication path anomalies, degraded GPU interconnects, and data loading and storage bottlenecks all appear green on traditional monitoring dashboards and only manifest under real workload pressure.
In distributed training, a small number of nodes whose kernel parameters are inconsistent with the rest can drag down the throughput of the entire training job, creating bottleneck nodes.
All Facts (4)
In large-scale distributed training, forward propagation, backward propagation, and AllReduce/AllGather communication operations are interleaved; if a communication operation is not complete, downstream compute operators must wait, creating bubbles.
50%UnverifiedCommunication primitives in distributed training (such as AllReduce gradient aggregation) can introduce non-deterministic accumulated errors due to differences in parallelism configuration and network topology.
50%UnverifiedHidden failures such as network topology asymmetry, collective communication path anomalies, degraded GPU interconnects, and data loading and storage bottlenecks all appear green on traditional monitoring dashboards and only manifest under real workload pressure.
50%UnverifiedIn distributed training, a small number of nodes whose kernel parameters are inconsistent with the rest can drag down the throughput of the entire training job, creating bottleneck nodes.
50%