The primary objective of parallel processing in distributed training is to achieve linear scalability. This means that the system's efficiency, measured by the number of samples processed per unit of time, should increase in direct proportion to the number of processing devices used.

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Parallelism is a fundamental strategy within distributed training that enhances efficiency. The core principle involves dividing the complex training problem into smaller, independent tasks that can be executed simultaneously across multiple computing devices.

Parallelism in Distributed LLM Training

Reference of Foundations of Large Language Models Course

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.

Types of Parallelism in LLM Training

Goal of Parallel Processing: Linear Scalability

The performance of a distributed training system is complex and is influenced by numerous factors beyond the specific parallelism method employed. These factors, including communication overhead, synchronization costs, fault tolerance, and numerical computation issues, can introduce bottlenecks that affect overall efficiency and prevent ideal performance gains.

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