The Compute Unified Device Architecture provides programming models, compilers, libraries, developer tools, and optimized kernels for Nvidia graphics processors. Its mature ecosystem supports AI, scientific computing, graphics, and other parallel workloads.
The platform’s advantage comes from accumulated software and expertise as well as hardware. Alternative chips must offer usable compilers, libraries, kernels, debugging, and deployment paths or find ways to generate that support efficiently.
ELI5
Compute Unified Device Architecture, usually called CUDA, is Nvidia's platform for using its graphics processors for general computing. It gives developers languages, libraries and tools that divide large jobs into many small calculations that run in parallel.
For example, an AI training program can use CUDA libraries to perform millions of matrix calculations on a Nvidia graphics processor. The hardware matters, but the large collection of tested software and developer knowledge also makes the platform useful.
