A GPU, or graphics processing unit, is a specialized processor designed to handle parallel computations. Originally built for rendering images and video, GPUs now power many compute intensive workloads such as machine learning and scientific simulations. GPUs contain thousands of smaller cores optimized for performing repeated operations efficiently. Unlike CPUs, which excel at sequential logic, GPUs are ideal for tasks that can be parallelized. Modern applications use GPUs for deep learning, 3D graphics, hardware acceleration, and data processing.
Key Characteristics
GPUs operate on large sets of data simultaneously, making them ideal for neural networks and matrix operations. They communicate with the CPU through high bandwidth buses and maintain their own dedicated memory. Frameworks such as CUDA and OpenCL give developers low level control over GPU computations. Cloud providers offer GPU instances for training and inference workloads. GPUs can significantly increase performance but require careful optimization to fully leverage their architecture.