CUDA

CUDA (Compute Unified Device Architecture) is a proprietary parallel computing platform and application programming interface (API) developed by Nvidia that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, significantly broadening their utility in artificial intelligence, scientific and high-performance computing. CUDA was created in 2004 and was officially released in 2007. When introduced, the name was an acronym for Compute Unified Device Architecture, but Nvidia later dropped the initial meaning 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. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers.

CUDA is written in the C programming language, but is designed to work with 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 support programming frameworks such as OpenMP, OpenACC and OpenCL.

Graphics processing unit

The graphics processing unit (GPU) is a specialized processor. It addresses the demands of real-time high-resolution, compute-intensive tasks such as 3D graphics. 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:

cryptographic hash functions

machine learning

molecular dynamics simulations

physics engines

History

CUDA traces to the early 2000s, when Ian Buck, a computer science PhD student at Stanford University, began experimenting with using GPUs for purposes beyond rendering graphics. Buck had become interested in GPUs during his undergraduate studies at Princeton University, initially through video gaming. After graduation, he interned at Nvidia, gaining deeper exposure to GPU architecture. At Stanford, he built an 8K gaming rig using 32 GeForce graphics cards, originally to push the limits of graphics performance in games like Quake and Doom. However, his interests shifted toward exploring the potential of GPUs for general-purpose parallel computing.

To that end, Buck developed Brook, a programming language designed to enable general-purpose computing on GPUs. His work attracted support from Nvidia and the Defense Advanced Research Projects Agency (DARPA). In 2004, Nvidia hired Buck and paired him with John Nickolls, then director of architecture for GPU computing. Together, they began transforming Brook into CUDA. CUDA was officially released in 2007.

CUDA became central to the company's strategy of positioning GPUs as versatile hardware for scientific applications. By 2015, CUDA's development increasingly focused on accelerating machine learning and artificial neural network workloads.

Ontology

The following table offers an approximate summary of the CUDA ontology.

Programming abilities

The CUDA platform is accessible to software developers through CUDA-accelerated libraries, compiler directives such as 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 cuPyNumeric 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):

cuBLAS – CUDA Basic Linear Algebra Subroutines library

CUDART – CUDA Runtime library

cuFFT – CUDA Fast Fourier Transform library

cuRAND – CUDA Random Number Generation library

cuSOLVER – CUDA based collection of dense and sparse direct solvers

cuSPARSE – CUDA Sparse Matrix library

NPP – NVIDIA Performance Primitives library

nvGRAPH – NVIDIA Graph Analytics library

NVML – NVIDIA Management Library

NVRTC – NVIDIA Runtime Compilation library for CUDA C++

CUDA 8.0 comes with these other software components:

nView – NVIDIA nView Desktop Management Software

NVWMI – NVIDIA Enterprise Management Toolkit

GameWorks PhysX – is a multi-platform game physics engine

CUDA 9.0–9.2 comes with these other components:

CUTLASS 1.0 – custom linear algebra algorithms,

NVIDIA Video Decoder was deprecated in CUDA 9.2; it is now available in NVIDIA Video Codec SDK

CUDA 10 comes with these other components:

nvJPEG – Hybrid (CPU and GPU) JPEG processing

CUDA 11.0–11.8 comes with these other components:

CUB is new one of more supported C++ libraries

MIG multi instance GPU support

nvJPEG2000 – JPEG 2000 encoder and decoder

Advantages

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

Scattered reads – code can read from arbitrary addresses in memory

Unified virtual memory (CUDA 4.0 and above)

Unified memory (CUDA 6.0 and above)

Shared memory – CUDA exposes a fast shared memory region that can be shared among threads. This can be used as a user-managed cache, enabling higher bandwidth than is possible using texture lookups.

Faster downloads and readbacks to and from the GPU

Full support for integer and bitwise operations, including integer texture lookups

Limitations…