What GPU Memory Meaning, Applications & Example
Dedicated memory for graphics processing units in AI computation.
What is GPU Memory?
GPU Memory refers to the dedicated memory in a graphics processing unit (GPU) that is used for storing and processing data during computational tasks. Unlike CPU memory, which handles general system operations, GPU memory is optimized for parallel processing tasks such as rendering graphics and performing complex computations for machine learning or AI applications.
Types of GPU Memory
- Global Memory: The main memory of the GPU used for storing large datasets or images.
- Shared Memory: A smaller, faster memory space within the GPU that is shared between different threads, typically used for intermediate data storage during computations.
- Texture Memory: A special type of memory used in graphics processing to store textures for rendering in 3D graphics.
- Constant Memory: A read-only memory used for values that do not change during kernel execution, such as constants in machine learning models.
Applications of GPU Memory
- Machine Learning: Used for storing training data, model parameters, and intermediate results during the training of neural networks.
- Computer Vision : Storing large images or video frames during real-time processing in applications like object detection or image classification .
- Gaming: GPU memory is critical for storing textures, models, and other assets to render high-quality graphics in video games.
Example of GPU Memory
In deep learning , a model’s weights, activation data, and gradients are stored in GPU memory to ensure that the computations can be parallelized, allowing faster model training compared to using CPU memory. During training, data batches are loaded into GPU memory, and after each iteration, results are processed and stored back in memory for the next computation.