A GPU (Graphics Processing Unit) is a specialized processor originally designed to accelerate graphics rendering. Unlike a CPU (Central Processing Unit), which handles a wide variety of general-purpose tasks, a GPU is optimized for handling many operations in parallel — especially those involving matrix and vector computations, which are common in image processing, 3D rendering, and more recently, machine learning.
Modern GPUs are used not only for gaming and visual effects but also for scientific computing, AI, data analysis, and cryptocurrency mining due to their high performance in parallel processing.
Parallel Processing Capability:
GPUs consist of hundreds or thousands of smaller cores designed for handling multiple tasks simultaneously.
Ideal for workloads that can be broken down into many smaller, identical operations (e.g., rendering each pixel of an image).
High Throughput:
Compared to CPUs, GPUs can process far more operations per second when working with large data sets, particularly in tasks like deep learning or simulations.
Efficient Performance in Graphics and Visualization:
Designed specifically to handle graphical computations such as texture mapping, shading, and frame buffering.
Essential for gaming, 3D modeling, and virtual reality.
Acceleration of Non-Graphics Tasks (GPGPU – General-Purpose computing on GPUs):
GPUs can be used for tasks like machine learning, financial modeling, and weather forecasting using platforms like CUDA or OpenCL.
Better Energy Efficiency (per operation):
For parallel workloads, GPUs often deliver better performance-per-watt compared to CPUs.
Improved AI and Deep Learning Performance:
Modern AI frameworks (e.g., TensorFlow, PyTorch) are optimized to run on GPUs, significantly reducing training times for models.
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