Role of CPU and GPU in training AI Models

When training an AI model, both GPUs (Graphics Processing Units) and CPUs (Central Processing Units) play distinct roles in accelerating the computation and improving training efficiency.

CPUs are general-purpose processors that excel at handling a wide range of tasks. They are designed to perform sequential calculations and are highly versatile. CPUs are responsible for managing the overall control flow, executing complex instructions, and coordinating various system components. In the context of AI model training, CPUs are involved in tasks such as data preprocessing, loading and storing data, managing memory, and coordinating the overall training process.

On the other hand, GPUs are specialized processors primarily designed for graphics rendering. However, their highly parallel architecture makes them well-suited for training AI models, which involve performing numerous similar computations simultaneously. GPUs contain thousands of smaller cores that can execute multiple instructions in parallel. This parallelization capability significantly accelerates training by allowing the GPU to process large amounts of data simultaneously.

CPU vs GPU

When training an AI model, the workflow typically involves the following steps:

1. Data Preprocessing: CPUs are responsible for tasks such as loading and preprocessing the training data, including data normalization, transformation, and feature extraction. These operations are often sequential and benefit from the CPU’s versatility.
2. Model Definition: Both CPUs and GPUs are involved in defining the architecture of the AI model. CPUs execute the code responsible for defining the model structure, connecting layers, and configuring hyperparameters. GPUs come into play during the training process, executing the computations required for forward and backward propagation.
3. Forward Propagation: During forward propagation, the input data passes through the layers of the neural network, and computations are performed to generate predictions. This step is highly parallelizable and benefits from the GPU’s ability to perform parallel calculations on large matrices.
4. Backward Propagation (Gradient Computation): In this step, the gradients of the model parameters are computed using techniques such as backpropagation. The gradients are essential for adjusting the model’s weights during training. GPUs excel at parallelizing these computations, as they involve matrix multiplications and element-wise operations.
5. Parameter Updates: After computing the gradients, the model’s parameters are updated using optimization algorithms such as gradient descent. This process involves both CPUs and GPUs, with CPUs managing the overall optimization process and GPUs performing the parallel computations required for parameter updates.
6. Memory Management: CPUs handle the management of memory resources during training, ensuring efficient data access and storage. They oversee tasks such as data transfer between main memory (RAM) and GPU memory (VRAM) to provide the necessary inputs and retrieve outputs.

The combination of CPUs and GPUs in AI model training leverages their respective strengths. CPUs handle sequential operations, data preprocessing, and overall system management, while GPUs excel at parallel computations, matrix operations, and accelerating training through massive parallelization. The parallel architecture and high memory bandwidth of GPUs make them particularly effective for deep learning models that involve complex architectures and large datasets. This is an example of the NVIDIA A100 GPU which is one of the best for AI. Also it has 432 tensor cores. Also to give some context, the most expensive Macbook pro currently has a 12 core CPU and a 38 core GPU.

https://www.nvidia.com/en-us/data-center/a100/

It’s important to note that the specific utilization of CPUs and GPUs during training depends on the software framework and libraries being used. Popular frameworks such as TensorFlow and PyTorch provide abstractions and APIs that automatically distribute computations between CPUs and GPUs, optimizing the training process based on the available hardware.

In summary, CPUs and GPUs work together in training AI models, with CPUs handling sequential tasks, memory management, and overall system coordination, while GPUs excel at parallel computations, accelerating training through massive parallelization. This collaborative approach harnesses the strengths of both processors to improve the efficiency and speed of AI model training.

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