CUDA Programming#
CUDA is NVIDIA’s parallel computing platform that enables developers to harness the massive parallelism of GPUs for general-purpose computing. Unlike CPUs optimized for sequential tasks with a few powerful cores, GPUs contain thousands of smaller cores designed for throughput-oriented workloads. This architecture excels at data-parallel problems where the same operation is applied to many elements simultaneously, such as matrix operations, image processing, and scientific simulations.
The CUDA programming model extends C++ with keywords for defining kernel functions that execute on the GPU and APIs for managing memory transfers between host (CPU) and device (GPU). Understanding memory hierarchy is crucial for performance: global memory has high bandwidth but high latency, while shared memory provides fast on-chip storage shared within thread blocks. Effective CUDA programming involves minimizing data transfers, maximizing parallelism, and optimizing memory access patterns.