DeepSeek manages distributed training across multiple GPUs by employing a strategy that optimally divides the workload among available processing units, ensuring that each GPU can efficiently contribute to the overall task. The primary mechanism is data parallelism, where large datasets are split into smaller batches. Each GPU is assigned one of these batches, allowing them to process data simultaneously. This method not only speeds up the training process but also enhances the model's ability to generalize by exposing it to a wider variety of samples in a shorter time.
To facilitate communication between GPUs, DeepSeek utilizes a host of coordination protocols. One common approach is the All-Reduce algorithm, which ensures that models on different GPUs maintain consistency by sharing their gradients during training. After each backward pass, gradients calculated on each GPU are averaged, synchronized, and then used to update the model weights. This process occurs iteratively, helping to maintain a unified model across all GPUs, which is essential for achieving optimal performance and preventing discrepancies that could arise from divergent training paths.
Furthermore, DeepSeek incorporates advanced techniques for fault tolerance and workload balancing. If one GPU becomes slower due to hardware limitations, the framework can redistribute tasks to other GPUs dynamically, allowing for uninterrupted training. Additionally, it monitors GPU utilization to better allocate resources, ensuring that all GPUs are effectively utilized, which prevents idle time and maximizes efficiency. Overall, these strategies combined allow DeepSeek to perform distributed training smoothly across multiple GPUs, leading to faster training times and improved model accuracy.