/ˌdʒiː-piː-ˈjuː/
n. “The processor built for crunching graphics and parallel tasks.”
GPU, short for Graphics Processing Unit, is a specialized processor designed to accelerate rendering of images, video, and animations for display on a computer screen. Beyond graphics, modern GPUs are also used for parallel computation in fields like machine learning, scientific simulations, and cryptocurrency mining.
Key characteristics of GPU include:
- Parallel Architecture: Contains thousands of smaller cores optimized for simultaneous operations, ideal for graphics pipelines and parallel workloads.
- Graphics Acceleration: Handles rendering tasks such as shading, texture mapping, and image transformations.
- Compute Capability: Modern GPUs support general-purpose computing (GPGPU) via APIs like CUDA, OpenCL, or DirectCompute.
- Memory: Equipped with high-speed VRAM (video RAM) for storing textures, frame buffers, and computation data.
- Integration: Available as discrete cards (PCIe) or integrated within CPUs (iGPU) for lower-power devices.
Conceptual example of GPU usage:
# Using a GPU for parallel computation (conceptual)
GPU cores = 2048
Task: process large image array
Each core handles a portion of the data simultaneously
Result: faster computation than CPU aloneConceptually, GPU is like having a team of thousands of workers all handling small pieces of a big task at the same time, instead of one worker (CPU) doing it sequentially.
In essence, GPU is essential for modern graphics rendering, video processing, and high-performance parallel computing, providing both visual acceleration and computational power beyond traditional CPUs.