In computing, CUDA (originally 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, an approach called general-purpose computing on GPUs (GPGPU). CUDA API and its runtime: The CUDA API is an extension of the C programming language that adds the ability to specify thread-level parallelism in C and also to specify GPU device specific operations (like moving data between the CPU and the GPU). 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. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications. CUDA is designed to work with programming languages such as C, C++, Fortran and Python. 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. CUDA was created by Nvidia in 2006. 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.

Background

The graphics processing unit (GPU), as a specialized computer processor, addresses the demands of real-time high-resolution 3D graphics compute-intensive tasks. By 2012, GPUs had evolved into highly parallel multi-core systems allowing efficient manipulation of large blocks of data. This design is more effective than general-purpose central processing unit (CPUs) for algorithms in situations where processing large blocks of data is done in parallel, such as: Ian Buck, while at Stanford in 2000, created an 8K gaming rig using 32 GeForce cards, then obtained a DARPA grant to perform general purpose parallel programming on GPUs. He then joined Nvidia, where since 2004 he has been overseeing CUDA development. In pushing for CUDA, Jensen Huang aimed for the Nvidia GPUs to become a general hardware for scientific computing. CUDA was released in 2006. Around 2015, the focus of CUDA changed to neural networks.

Ontology

The following table offers a non-exact description for the ontology of CUDA framework.

Programming abilities

The CUDA platform is accessible to software developers through CUDA-accelerated libraries, compiler directives such as [OpenACC](https://bliptext.com/articles/openacc), and extensions to industry-standard programming languages including C, C++, Fortran and Python. C/C++ programmers can use 'CUDA C/C++', compiled to PTX with nvcc, Nvidia's LLVM-based C/C++ compiler, or by clang itself. Fortran programmers can use 'CUDA Fortran', compiled with the PGI CUDA Fortran compiler from The Portland Group. Python programmers can use the cuNumeric library to accelerate applications on Nvidia GPUs. In addition to libraries, compiler directives, CUDA C/C++ and CUDA Fortran, the CUDA platform supports other computational interfaces, including the Khronos Group's OpenCL, Microsoft's DirectCompute, OpenGL Compute Shader and C++ AMP. Third party wrappers are also available for Python, Perl, Fortran, Java, Ruby, Lua, Common Lisp, Haskell, R, MATLAB, IDL, Julia, and native support in Mathematica. In the computer game industry, GPUs are used for graphics rendering, and for game physics calculations (physical effects such as debris, smoke, fire, fluids); examples include PhysX and Bullet. CUDA has also been used to accelerate non-graphical applications in computational biology, cryptography and other fields by an order of magnitude or more. CUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source). The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0, which supersedes the beta released February 14, 2008. CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most standard operating systems. CUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order): CUDA 8.0 comes with these other software components: CUDA 9.0–9.2 comes with these other components: CUDA 10 comes with these other components: CUDA 11.0–11.8 comes with these other components:

Advantages

CUDA has several advantages over traditional general-purpose computation on GPUs (GPGPU) using graphics APIs:

Limitations

Example

This example code in C++ loads a texture from an image into an array on the GPU: Below is an example given in Python that computes the product of two arrays on the GPU. The unofficial Python language bindings can be obtained from PyCUDA. Additional Python bindings to simplify matrix multiplication operations can be found in the program pycublas. while CuPy directly replaces NumPy:

GPUs supported

Supported CUDA Compute Capability versions for CUDA SDK version and Microarchitecture (by code name): Note: CUDA SDK 10.2 is the last official release for macOS, as support will not be available for macOS in newer releases. CUDA Compute Capability by version with associated GPU semiconductors and GPU card models (separated by their various application areas): '*' – OEM-only products

Version features and specifications

Data types

Note: Any missing lines or empty entries do reflect some lack of information on that exact item.

Tensor cores

Note: Any missing lines or empty entries do reflect some lack of information on that exact item.

Technical specification

Multiprocessor architecture

For more information read the Nvidia CUDA programming guide.

Current and future usages of CUDA architecture

Comparison with competitors

CUDA competes with other GPU computing stacks: Intel OneAPI and AMD ROCm. Whereas Nvidia's CUDA is closed-source, Intel's OneAPI and AMD's ROCm are open source.

Intel OneAPI

oneAPI is an initiative based in open standards, created to support software development for multiple hardware architectures. The oneAPI libraries must implement open specifications that are discussed publicly by the Special Interest Groups, offering the possibility for any developer or organization to implement their own versions of oneAPI libraries. Originally made by Intel, other hardware adopters include Fujitsu and Huawei.

Unified Acceleration Foundation (UXL)

Unified Acceleration Foundation (UXL) is a new technology consortium working on the continuation of the OneAPI initiative, with the goal to create a new open standard accelerator software ecosystem, related open standards and specification projects through Working Groups and Special Interest Groups (SIGs). The goal is to offer open alternatives to Nvidia's CUDA. The main companies behind it are Intel, Google, ARM, Qualcomm, Samsung, Imagination, and VMware.

AMD ROCm

ROCm is an open source software stack for graphics processing unit (GPU) programming from Advanced Micro Devices (AMD).