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CUDA
Sign in to saveCUDA (Compute Unified Device Architecture) 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 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.
Key facts
- Software.name
- CUDA
- Software.screenshot
- Nvidia CUDA Logo.jpg
- Software.author
- Ian BuckJohn Nickolls
- Software.developer
- Nvidia
- Software.programming language
- C
- Software.operating_system
- Windows, Linux
- Software.platform
- Supported GPUs
- Software.genre
- GPGPU
- Software.license
- Proprietary
via Wikipedia infobox
Article
23 sectionsContents
- Background
- Ontology
- Programming abilities
- Advantages
- Limitations
- Example
- GPUs supported
- Version features and specifications
- Data types
- Floating-point types
- Version support
- Tensor cores
- Technical specifications
- Multiprocessor architecture
- Usages of CUDA architecture
- Comparison with competitors
- Intel OneAPI
- Unified Acceleration Foundation (UXL)
- AMD ROCm
- See also
- References
- Further reading
- External links
CUDA (Compute Unified Device Architecture) 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 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.