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Trade-off of Micro-batch Size in Pipeline Parallelism
While the goal of micro-batching in pipeline parallelism is to maximize the number of batches to reduce worker idle time, there is a practical trade-off. Using excessively small micro-batches can be detrimental, leading to reduced GPU utilization and higher costs associated with task-switching. Consequently, this can negatively impact the overall throughput of the training system.
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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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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
Learn After
A team is training a large computational model by splitting a batch of data into many smaller chunks and processing them sequentially across multiple hardware stages. They observe that as they decrease the size of these chunks, the overall training speed initially increases. However, when they make the chunks extremely small, the training speed unexpectedly begins to drop. What is the most likely cause for this drop in performance at extremely small chunk sizes?
Optimizing Pipelined Training Throughput
Analyzing the Impact of Chunk Size on Training Throughput
A team is training a large computational model using a pipelined approach where a data batch is divided into smaller chunks for sequential processing across multiple hardware stages. They test two strategies:
- Strategy X: Uses a very large number of extremely small chunks.
- Strategy Y: Uses a moderate number of medium-sized chunks.
They observe that Strategy Y results in a significantly higher overall training throughput. Which of the following statements provides the most accurate evaluation of this outcome?