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Micro-batching in Pipeline Parallelism
The core mechanism of pipeline parallelism involves partitioning a data batch into several smaller 'micro-batches'. These micro-batches are then fed sequentially into the pipeline of workers. As soon as a worker completes its computation for one micro-batch and forwards it to the subsequent worker, it immediately begins processing the next available micro-batch. This continuous flow ensures that different stages of the computation are active simultaneously, maximizing device utilization.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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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...
You are on-call for an internal LLM training platf...
Your team is training a 70B-parameter LLM on 8 GPU...
You’re advising an internal platform team that mus...
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
Learn After
Trade-off of Micro-batch Size in Pipeline Parallelism
Consider a computational process distributed across four sequential stages (S1, S2, S3, S4), each on a different device. A large data batch is partitioned into smaller, uniform 'micro-batches' (MB1, MB2, MB3, etc.) to be processed in a continuous flow. At a particular point in time, device S3 has just completed its work on MB1 and passed it to S4. What is the activity of device S1 at this exact moment, assuming the pipeline is running efficiently and has been for some time?
Pipeline Efficiency Analysis
Mechanism of Utilization Improvement in Pipelined Systems