CUDA

Extracted Attributes

• Type

  Proprietary parallel computing platform and API

• Purpose

  Accelerated general-purpose processing on GPUs (GPGPU)

• Developer

  Nvidia

• Key Components

  Software layer, API library, compilers, libraries, developer tools

• Original Acronym

  Compute Unified Device Architecture

• Significance (Jensen Huang)

  Foundational element of the 'American tech stack' and crucial to Nvidia's innovation in the AI era

• Primary Programming Language

  C

• Supported Programming Languages

  C++, Fortran, Python, Julia

Timeline

• Nvidia began creating CUDA. (Source: Wikipedia)

  2004

• CUDA was officially released. (Source: Summary, Wikipedia, Wikidata)

  2007-06-23

CUDA

CUDA is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, significantly broadening their utility in scientific and high-performance computing. CUDA was created by Nvidia starting in 2004 and was officially released by in 2007. When it was first introduced, the name was an acronym for Compute Unified Device Architecture, but Nvidia later dropped the common use of the acronym and now rarely expands it. CUDA is both a software layer that manages data, giving direct access to the GPU and CPU as necessary and a library of APIs that enable parallel computation for various needs. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications. CUDA is written in C but is designed to work with a wide array of other programming languages including C++, Fortran, Python and Julia. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which require advanced skills in graphics programming. CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC and OpenCL.

Web Search Results

• CUDA - Wikipedia

  CUDA is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing

• CUDA C++ Programming Guide

  The compute capability version of a particular GPU should not be confused with the CUDA version (for example, CUDA 7.5, CUDA 8, CUDA 9), which is the version of the CUDA software platform. The CUDA platform is used by application developers to create applications that run on many generations of GPU architectures, including future GPU architectures yet to be invented. While new versions of the CUDA platform often add native support for a new GPU architecture by supporting the compute capability [...] As mentioned in Heterogeneous Programming, the CUDA programming model assumes a system composed of a host and a device, each with their own separate memory. Kernels operate out of device memory, so the runtime provides functions to allocate, deallocate, and copy device memory, as well as transfer data between host memory and device memory. Device memory can be allocated either as linear memory or as CUDA arrays. [...] CUDA arrays are opaque memory layouts optimized for texture fetching. They are described in Texture and Surface Memory. Linear memory is allocated in a single unified address space, which means that separately allocated entities can reference one another via pointers, for example, in a binary tree or linked list. The size of the address space depends on the host system (CPU) and the compute capability of the used GPU: Table 4 Linear Memory Address Space

• GPU Accelerated Computing with C and C++ - NVIDIA Developer

  # GPU Accelerated Computing with C and C++ Using the CUDA Toolkit you can accelerate your C or C++ applications by updating the computationally intensive portions of your code to run on GPUs. To accelerate your applications, you can call functions from drop-in libraries as well as develop custom applications using languages including C, C++, Fortran and Python. Below you will find some resources to help you get started using CUDA. [...] You are now ready to write your first CUDA program. The article, Even Easier Introduction to CUDA, introduces key concepts through simple examples that you can follow along. The video below walks through an example of how to write an example that adds two vectors. The Programming Guide in the CUDA Documentation introduces key concepts covered in the video including CUDA programming model, important APIs and performance guidelines. [...] Install the free CUDA Toolkit on a Linux, Mac or Windows system with one or more CUDA-capable GPUs. Follow the instructions in the CUDA Quick Start Guide to get up and running quickly. Or, watch the short video below and follow along.

• CUDA-X GPU-Accelerated Libraries - NVIDIA Developer

  # NVIDIA CUDA-X Libraries NVIDIA CUDA-X™ Libraries, built on CUDA®, is a collection of libraries that deliver dramatically higher performance—compared to CPU-only alternatives—across application domains, including AI and high-performance computing. [...] GPU-accelerated libraries for image and video decoding, encoding, and processing that use CUDA and specialized hardware components of GPUs. ### RAPIDS cuCIM Accelerate input/output (IO), computer vision, and image processing of n-dimensional, especially biomedical images. ### CV-CUDA Open-source library for high-performance, GPU-accelerated pre- and post-processing in vision AI pipelines. ### NVIDIA DALI [...] GPU-accelerated library of C++ parallel algorithms and data structures. ### Computational Lithography Library Targeting the modern-day challenges of nanoscale computational lithography. #### cuLitho Library with optimized tools and algorithms to accelerate computational lithography and the manufacturing of semiconductors using GPUs. ## Quantum Libraries Enabling simulation, HPC integration and AI for quantum computing. ### cuQuantum

• CUDA Definition & Meaning - Merriam-Webster

  link icon link icon link icon Definition Definition # cuda ## noun ## Word History short for barracuda ### The Ultimate Dictionary Awaits Expand your vocabulary and dive deeper into language with Merriam-Webster Unabridged. Discover what makes Merriam-Webster Unabridged the essential choice for true word lovers. ## Browse Nearby Words ## Cite this Entry “Cuda.” Merriam-Webster.com Dictionary, Merriam-Webster, Accessed 26 Jul. 2025. ## Share [...] Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free! ## More from Merriam-Webster Play Quordle: Guess all four words in a limited number of tries. Each of your guesses must be a real 5-letter word. ### Can you solve 4 words at once? ### Can you solve 4 words at once? #### Word of the Day #### embellish Listen to the pronunciation of embellish See Definitions and Examples » Get Word of the Day daily email! [...] Play Quordle: Guess all four words in a limited number of tries. Each of your guesses must be a real 5-letter word. Play Blossom: Solve today's spelling word game by finding as many words as you can using just 7 letters. Longer words score more points. Play Missing Letter: a crossword with a twist. Each of the 25 puzzle words start with a different letter of the alphabet. Which letter is missing? anagrams on a chalk board Learn a new word every day. Delivered to your inbox!

• Image

  

• Instance Of

  Q165194

• Inception Date

  6/23/2007

CUDA (or Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for general purpose processing, an approach called general-purpose computing on GPUs (GPGPU). CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. CUDA is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC and OpenCL; and HIP by compiling such code to CUDA. CUDA was created by Nvidia. When it was first introduced, the name was an acronym for Compute Unified Device Architecture, but Nvidia later dropped the common use of the acronym.