CUDA (from English Compute Unified Device Architecture) is a parallel computing architecture developed by NVIDIA for performing computations using graphics processing units (GPUs). The technology allows the use of GPU resources not only for graphics rendering but also for general-purpose tasks, including simulation, machine learning, scientific computing, and big data processing.
Principle of Operation
CUDA provides developers with an application programming interface (API) and tools that enable writing code in C, C++, Fortran, Python, and other languages to be executed directly on the GPU.
A GPU contains thousands of cores capable of performing operations in parallel, making it significantly more efficient than a central processing unit (CPU) when handling tasks that require simultaneous processing of large data volumes.
Programs created with CUDA typically consist of two parts:
- code executed on the CPU, responsible for overall logic and control;
- code executed on the GPU, processing data arrays in parallel threads (known as CUDA cores).
Applications
CUDA is widely used in areas requiring high computational performance, such as:
- training neural networks and data processing in AI/ML projects;
- graphics rendering and video post-processing;
- physical simulations, bioinformatics, and financial analysis;
- big data analytics and large-scale image processing.
For example, most modern machine learning libraries — such as TensorFlow, PyTorch, and MXNet — support CUDA acceleration, which significantly increases model training speed.
Advantages
The main advantage of CUDA is its ability to perform high-speed parallel computations. This allows processing large data volumes faster and with lower resource consumption. In addition, the architecture is supported by all modern NVIDIA graphics cards, making it the de facto standard for GPU acceleration.