Context:
As the world rushes to make use of the latest wave of AI technologies, one piece of high-tech hardware has become a surprisingly hot commodity: the graphics processing unit, or GPU.
Relevance:
GS III: Science and Technology
Dimensions of the Article:
- About Graphics Processing Unit (GPU)
- Applications
About Graphics Processing Unit (GPU):
- Definition: A Graphics Processing Unit (GPU) is a specialized computer chip designed to render graphics and images by performing rapid mathematical calculations.
- Usage: GPUs are utilized in both professional and personal computing environments. Originally, they were responsible for rendering 2D and 3D images, animations, and videos.
- Component: Similar to a central processing unit (CPU), a GPU is a chip component in computing devices. However, it is specifically optimized to handle and accelerate graphics workloads and display graphics content on devices such as PCs or smartphones.
- Architecture: Modern CPUs typically consist of 8 to 16 cores capable of processing complex tasks sequentially. In contrast, GPUs have thousands of relatively small cores designed to work simultaneously (“in parallel”) to achieve fast overall processing. This architecture is well-suited for tasks requiring a large number of simple operations performed simultaneously.
How a GPU works:
- Parallel Processing: GPUs employ parallel processing, where multiple processors handle separate parts of a single task simultaneously.
- Dedicated RAM: GPUs have their own RAM specifically designed to store the large amounts of data processed for intensive graphics tasks.
- Graphics Pipeline: For graphics applications, the CPU sends instructions to the GPU for drawing graphics content on screen. The GPU executes these instructions in parallel and at high speeds to display the content on the device, a process known as the graphics or rendering pipeline.
Applications:
- Creative Content Production: GPUs are used for tasks such as video editing, rendering, and graphic design.
- High-Performance Computing (HPC): GPUs play a crucial role in accelerating scientific simulations, data analysis, and other computationally intensive tasks.
- Artificial Intelligence (AI): GPUs are essential for training and deploying machine learning and deep learning models due to their ability to perform parallel computations efficiently.
- Offloading Tasks: GPUs were initially developed to offload graphics-related tasks from CPUs, allowing for faster and smoother rendering of content on computer screens.
-Source: The Hindu
