Tensor-Parallelisierung is a method used in verteiltes Rechnen, particularly in the context of training large künstliche Intelligenz (AI) models. It involves splitting the computations associated with tensors—multi-dimensional arrays that represent data and parameters in neural networks—across multiple processors or devices. This allows for more efficient processing and faster training times.
In einer typischen Konfiguration wird ein großes neuronales Netzwerkmodell is divided into smaller parts, with each part assigned to different processors. Each processor handles a portion of the tensor operations, allowing the overall computation to be executed simultaneously. This is particularly useful for handling the large amounts of data and complex calculations involved in deep learning.
Tensoren im Deep Learning können verschiedene Elemente darstellen, wie Eingaben, weights, and outputs of a neural network. When using tensor parallelism, these tensors are partitioned based on different strategies, such as splitting along specific dimensions. For instance, if a tensor represents a matrix of weights, tensor parallelism might involve dividing the matrix into smaller matrices that can be processed in parallel.
This approach not only speeds up the training process but also enables the handling of larger models than would be feasible on a single device. By distributing the load, tensor parallelism helps to optimize resource utilization and can lead to lower training costs in terms of time and computational power. However, implementing tensor parallelism requires careful orchestration to ensure that the processors work together efficiently and that data is correctly synchronized between them.