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  2. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, [15] Uber's Pyro, [16] Hugging Face's Transformers, [17] PyTorch Lightning, [18] [19] and Catalyst. [20] [21] PyTorch provides two high-level features: [22] Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    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. [5] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

  4. List of 3D graphics libraries - Wikipedia

    en.wikipedia.org/wiki/List_of_3D_graphics_libraries

    These APIs have also proved vital to computer graphics hardware manufacturers, as they provide a way for programmers to access the hardware in an abstract way, while still taking advantage of the special hardware of any specific graphics card. The first 3D graphics framework was probably Core, published by the ACM in 1977.

  5. ROCm - Wikipedia

    en.wikipedia.org/wiki/ROCm

    ROCm [3] is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing.

  6. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    Torch has been extended for use on Android [12] [better source needed] and iOS. [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14] Facebook has released a set of extension modules as open source software. [15]

  7. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  8. 11 must-see astronomy events in 2025 - AOL

    www.aol.com/weather/11-must-see-astronomy-events...

    The new year will be a busy one in the night sky with celestial sights of all types for everyone to enjoy, many of which can be viewed without needing a telescope or traveling hundreds of miles to ...

  9. General-purpose computing on graphics processing units

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

    Given sufficient graphics processing power even graphics programmers would like to use better formats, such as floating point data formats, to obtain effects such as high-dynamic-range imaging. Many GPGPU applications require floating point accuracy, which came with video cards conforming to the DirectX 9 specification.