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GPU

As explained in Deep Learning with R, Third Edition

A GPU is a massively parallel processor designed primarily for graphics computation and also widely used for numerical machine learning workloads.

A GPU is a highly parallel processor used for graphics and numerical workloads. Deep-learning frameworks use GPUs to run tensor computations and train models. section 3.1 section 20.1.4

Text tokenization runs on the CPU because GPUs handle numeric inputs. Parallel preprocessing with tf.data helps keep batches ready for the GPU. section 14.4.1

CUDA's release in 2006 made neural networks on consumer GPUs possible. Deep-learning frameworks later combined GPU computation with automatic differentiation and multi-device execution. section 3.1

How Deep Learning with R, Third Edition covers it

How deep it goes
Explains GPU tensor computation, model training, and CPU-side preprocessing constraints.
Examples
Shows CPU-side text tokenization and parallel preprocessing, and traces consumer gaming GPUs into deep-learning hardware.
What you can do after
Understand why tokenization stays on the CPU and how parallel preprocessing helps prevent GPUs waiting for input.
How it connects
Links GPU execution to CPUs, TPUs, CUDA, tensor computation, automatic differentiation, and deep-learning frameworks.
Cover of Deep Learning with R, Third Edition
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This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.