How NVIDIA's platform lets programs use GPUs for general-purpose parallel computation.
What is CUDA?
CUDA is NVIDIA's platform for using compatible GPUs for general-purpose computation as well as graphics.
It includes a programming model, compiler tools, libraries, runtime APIs and hardware execution concepts.
How does it work?
A program launches a kernel over many threads. Threads are grouped into blocks, and blocks run on streaming multiprocessors.
Threads can share fast local memory within a block, while larger global memory is shared across the device.
Why is it important?
CUDA made GPU acceleration accessible to scientific computing, simulation, image processing, data analytics and machine learning.
Libraries for linear algebra, deep learning and communication let users benefit without writing every low-level operation.
Limits
A problem must contain enough parallel work to offset data transfer, synchronization and programming overhead.
Performance depends heavily on memory access, branching, precision and hardware generation. CUDA is powerful but tied to a vendor ecosystem, so portability and long-term access matter.
Ai disclosure: written with the help of AI (ChatGPT). You are encouraged to point out errors and omissions.
