Micro-batching in Pipeline Parallelism

Illustration of Pipeline Parallelism with Micro-batches

A large neural network model is partitioned across four sequential processing stages, with each stage running on a separate hardware device. During training, a full batch of data is processed entirely by the first device, and its output is then passed to the second device. The second device processes this output and passes its result to the third, and so on. While one device is actively computing, the other three devices are idle, waiting for their turn. What is the primary inefficiency this specific computational strategy introduces?

A large computational model is partitioned across two hardware devices (Device 1 and Device 2) in a sequential pipeline. To improve efficiency, a data batch is divided into two smaller micro-batches. Arrange the following events in the correct chronological order to accurately represent the flow of computation that maximizes hardware utilization.

Optimizing Training Efficiency for a Large Model

Your team must train a 30B-parameter LLM on a sing...

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Designing a Distributed Training Plan Under Memory, Throughput, and Stability Constraints

Postmortem and Redesign of a Distributed LLM Training Run with Divergence and Low GPU Utilization

Diagnosing a Scaling Regression in Hybrid Parallel LLM Training

Stabilizing and Scaling an LLM Training Job Across Two GPU Clusters

Choosing a Distributed Training Configuration After a Hardware Refresh

Selecting a Hybrid Parallelism + Mixed-Precision Strategy for a Memory-Bound LLM Training Run