Home > AI Solutions > Gen AI > Guides > Design Guide—Generative AI in the Enterprise – Model Customization > Parallelism
Parallelism in distributed computing is crucial for enhancing performance and scalability, especially in large-scale language model (LLM) training tasks. It enables multiple computations to occur simultaneously across GPUs, thereby reducing overall computation time and improving efficiency. There are several types of parallelism used in LLM training, including data parallelism, tensor parallelism, and pipeline parallelism.
Data parallelism divides a large dataset into smaller chunks and distributes these chunks across multiple GPUs. Each GPU then independently processes its assigned portion of the data, typically performing the same computation on different subsets of the dataset. This approach is particularly effective for LLMs that require the same operations to be applied to different parts of the dataset in parallel to update model parameters during training.
Tensor parallelism is a specialized form of parallelism in deep learning frameworks. In tensor parallelism, individual operations on tensors (multi-dimensional arrays) are distributed across multiple GPUs, with each GPU responsible for computing a specific portion of the tensor. This method enables efficient use of GPU resources and accelerates the training process of large-scale LLMs.
Pipeline parallelism breaks down the training process into a series of smaller stages or steps, with different GPUs running each stage concurrently. The output of one stage serves as the input to the next stage, forming a pipeline of processing stages. By overlapping the running of different stages, pipeline parallelism minimizes idle time and maximizes GPU use, leading to faster overall computation times for LLM training tasks.
Understanding and selecting the appropriate parallelism techniques is paramount for optimizing the performance of LLM customization. In the performance section (insert link), we delve into insights for choosing the right technique based on factors such as the model architecture, customization method, and the specifications of servers and GPUs used in the training process. By using the most suitable parallelism strategies, practitioners can maximize the efficiency and scalability of LLM training, ultimately enhancing model performance and accelerating the customization process.