Basic_principles_distributed_training

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title: ‘Essential Mechanics of Distributed Deep Learning’ description: Principles that every distributed training algorithm follows. date: 2025-06-13 00:55:00 +0530 author: skrohit categories: [distributed-training, deep-learning] tags: [llms, transformers] pin:false —

What does any distributed training algorithm do?

Every distributed training algorithm aims to train a deep learning model faster by utilizing multiple devices (GPUs or TPUs). Multiple devices are required because the size of the model and the amount of data being processed are often too large to fit into the memory of a single device. Whenever multiple devices coordinate together to perform a computational task, they need to communicate with each other.

Every distributed training algorithm must follow these principles:

• Efficient communication: The algorithm should minimize the amount of data that needs to be communicated between devices. This is crucial because communication can be a bottleneck in distributed training.
• Computation and communication overlap: The algorithm should allow for computation and communication to happen simultaneously, reducing idle time for devices.
• Analyse correct implementation: The algorithm should ensure that the model parameters are updated correctly across all devices, maintaining consistency in the model state.