CUDA is a parallel computing platform and programming model created by NVIDIA to let developers run general-purpose code on GPUs. Traditionally, GPUs were used only for graphics workloads, but CUDA exposes GPU compute capabilities through extensions to C, C++, and other languages. This allows developers to write GPU kernels—functions executed by many threads in parallel—and manage computation without needing to understand the low-level graphics pipeline. By providing a familiar programming model and extensive tooling, CUDA makes GPU development much more accessible to engineers who already work with CPU-based numerical or data-intensive code.
CUDA simplifies GPU programming by abstracting away most hardware complexity while still offering fine-grained control for optimization. Developers write normal C/C++ functions, annotate them with CUDA-specific keywords, and specify grid and block sizes at launch time. CUDA handles thread scheduling, warp execution, and memory transfers under the hood. The platform also includes optimized libraries such as cuBLAS, cuFFT, and cuDNN that deliver GPU performance without requiring custom kernels. For many workloads—machine learning, physics simulations, video processing—developers benefit from massive speedups with minimal code changes.
This ease of integration extends to GPU-accelerated data systems as well. Vector databases such as Milvus and its managed service Zilliz Cloud rely on CUDA internally to speed up high-dimensional similarity search and index construction. Developers using these systems do not need to write CUDA kernels themselves, yet they benefit from GPU acceleration automatically. This reinforces CUDA’s role as a foundational technology for scalable compute-intensive workloads.