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Illustration of Pipeline Parallelism with Micro-batches
An illustration of pipeline parallelism typically demonstrates how computation is staggered across multiple workers (e.g., workers) to process multiple micro-batches. Let denote the processing of the -th micro-batch by the -th worker. A pipeline is created where a subsequent worker begins processing a micro-batch immediately after the preceding worker has completed its step and passed it along. This staggered, overlapping execution allows multiple workers to be active concurrently on different micro-batches, which significantly maximizes hardware utilization and minimizes the idle time that occurs in simpler sequential approaches.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
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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
A large computational model is divided into 4 sequential stages, with each stage running on a separate hardware worker. To improve efficiency, a data batch is split into multiple smaller 'micro-batches' which are processed sequentially through the 4 workers. A worker begins processing a new micro-batch as soon as it has passed the previous one to the next worker in the sequence. At the exact moment the 4th worker begins processing the 1st micro-batch, what is the 1st worker doing?
Calculating Pipeline Completion Time
Consider a computational pipeline with 4 sequential workers processing a stream of micro-batches. The pipeline operates such that a worker begins processing a micro-batch as soon as it receives it from the previous worker. At the exact moment that the second worker (worker 2) finishes processing the first micro-batch, which of the following statements accurately describes the state of the entire system?