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Shared memory – CUDA exposes a fast shared memory region that can be shared among threads.Unified virtual memory (CUDA 4.0 and above).

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Scattered reads – code can read from arbitrary addresses in memory.

nvJPEG2000 – JPEG 2000 encoder and decoderĬUDA has several advantages over traditional general-purpose computation on GPUs (GPGPU) using graphics APIs:.

CUB is new one of more supported C++ libraries.

nvJPEG – Hybrid (CPU and GPU) JPEG processingĬUDA 11.0-11.8 comes with these other components:.

NVCUVID – NVIDIA Video Decoder was deprecated in CUDA 9.2 it is now available in NVIDIA Video Codec SDKĬUDA 10 comes with these other components:.

CUTLASS 1.0 – custom linear algebra algorithms,.

GameWorks PhysX – is a multi-platform game physics engineĬUDA 9.0–9.2 comes with these other components:.

NVWMI – NVIDIA Enterprise Management Toolkit.

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nView – NVIDIA nView Desktop Management Software.

NVRTC – NVIDIA Runtime Compilation library for CUDA C++ĬUDA 8.0 comes with these other software components:.

nvGRAPH – NVIDIA Graph Analytics library.

NPP – NVIDIA Performance Primitives library.

cuSOLVER – CUDA based collection of dense and sparse direct solvers.

cuRAND – CUDA Random Number Generation library.

cuFFT – CUDA Fast Fourier Transform library.

cuBLAS – CUDA Basic Linear Algebra Subroutines library.

CUDA is compatible with most standard operating systems.ĬUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order):

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CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line.

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Mac OS X support was later added in version 2.0, which supersedes the beta released February 14, 2008. The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux.

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ĬUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source). CUDA has also been used to accelerate non-graphical applications in computational biology, cryptography and other fields by an order of magnitude or more. 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. 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 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. Fortran programmers can use 'CUDA Fortran', compiled with the PGI CUDA Fortran compiler from The Portland Group. C/C++ programmers can use 'CUDA C/C++', compiled to PTX with nvcc, Nvidia's LLVM-based C/C++ compiler, or by clang itself. 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++ and Fortran.

Copy the resulting data from GPU memory to main memory.

GPU's CUDA cores execute the kernel in parallel.Copy data from main memory to GPU memory.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.

CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC and OpenCL and HIP by compiling such code to CUDA.ĬUDA was created by Nvidia. 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.

ĬUDA is designed to work with programming languages such as C, C++, and Fortran.

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 (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).