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  2. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI.

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    In computing, CUDA is a proprietary [1] 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.

  4. Albumentations - Wikipedia

    en.wikipedia.org/wiki/Albumentations

    Albumentations is an open-source image augmentation library created in June 2018 by a group of researchers and engineers, including Alexander Buslaev, Vladimir Iglovikov, and Alex Parinov.

  5. Numba - Wikipedia

    en.wikipedia.org/wiki/Numba

    Numba is an open-source JIT compiler that translates a subset of Python and NumPy into fast machine code using LLVM, via the llvmlite Python package.It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.

  6. OptiX - Wikipedia

    en.wikipedia.org/wiki/OptiX

    The computations are offloaded to the GPUs through either the low-level or the high-level API introduced with CUDA. CUDA is only available for Nvidia's graphics products. Nvidia OptiX is part of Nvidia GameWorks. OptiX is a high-level, or "to-the-algorithm" API, meaning that it is designed to encapsulate the entire algorithm of which ray ...

  7. PhysX - Wikipedia

    en.wikipedia.org/wiki/PhysX

    A BFG Physx card. PhysX is an open-source [1] realtime physics engine middleware SDK developed by Nvidia as part of the Nvidia GameWorks software suite.. Initially, video games supporting PhysX were meant to be accelerated by PhysX PPU (expansion cards designed by Ageia).

  8. General-purpose computing on graphics processing units

    en.wikipedia.org/wiki/General-purpose_computing...

    General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).

  9. OpenACC - Wikipedia

    en.wikipedia.org/wiki/OpenACC

    IPMACC [21] is an open source C compiler developed by University of Victoria that translates OpenACC to CUDA, OpenCL, and ISPC. Currently, only following directives are supported: data, kernels, loop, and cache. GCC support for OpenACC was slow in coming. [22]