The Compute Unified Device Architecture (CUDA) refers to a processing unit in the NVIDIA GPU architecture that is specifically used to perform parallel computing tasks under the CUDA programming model. The following is a brief introduction to how CUDA works:

How CUDA works:
GPU parallel computing architecture:
    NVIDIA's Gpus are designed with an architecture for massively parallel computing, with each GPU containing a large number of CUDAs. These are organized in the form of Streaming Multiprocessors (SM), each of which contains multiple CUDAs.

The concept of thread bundle (Warp) :
    In the CUDA programming model, threads are the basic execution unit. CUDA executes instructions as a thread bundle (Warp). A thread bundle typically contains 32 threads that share the same instruction stream and execute in parallel.

SIMD execution model:
    Each CUDA uses SIMD (Single Instruction Multiple Data) execution model, that is, each CUDA can execute the same instruction on multiple data at the same time. This approach enables Gpus to efficiently handle large-scale data parallel computing tasks.

Data parallelism:
    CUDA is designed for data parallel computing. For example, a CUDA can perform the same operation on multiple pixels, matrix elements, or other data at the same time, speeding up complex numerical computation tasks.

Scheduling and Resource management:

    CUDA is managed by a scheduler inside the GPU that allocates thread bundles and other resources based on the demands of the task. Each CUDA can dynamically adjust its resource usage to maximize throughput and efficiency.

Memory and memory models:
    CUDA has access to global and shared memory on the GPU, and these memory models make it possible to efficiently share data and communicate and synchronize between different ones.