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Types of Parallelism in LLM Training
In the context of training Large Language Models, parallelism can be implemented through several distinct approaches. The primary forms include data parallelism, model parallelism, tensor parallelism, and pipeline parallelism.
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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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Types of Parallelism in LLM Training
Goal of Parallel Processing: Linear Scalability
Complexity of Distributed Training
A research lab is training a language model so large that it would take several years to complete on a single computer. To speed up the process, they decide to use a cluster of 1,000 interconnected computers. Which of the following statements best analyzes the fundamental principle that allows this cluster to significantly reduce the training time?
Evaluating a Training Strategy
Explaining Training Efficiency
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Data Parallelism
Model Parallelism
Pipeline Parallelism
A research team is developing a novel language model with several trillion parameters. During the initial training setup, they discover that the model is too large to fit into the memory of a single available accelerator (e.g., a GPU). Which parallelism strategy is specifically designed to address this fundamental constraint?
Match each parallelism strategy with the description that best defines its core mechanism for distributing the training workload.
Diagnosing Training Inefficiency