enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  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. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    Format name Design goal Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration

  4. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    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. [ 6 ]

  5. List of OpenCL applications - Wikipedia

    en.wikipedia.org/wiki/List_of_OpenCL_applications

    PyOpenCL, [122] Python interface to OpenCL API Project Coriander: Conversion CUDA to OpenCL 1.2 with CUDA-on-CL [ 123 ] [ 124 ] Lightweight Java Game Library (LWJGL) contains low-lag Java bindings for OpenCL

  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. DeepSpeed - Wikipedia

    en.wikipedia.org/wiki/DeepSpeed

    The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training.

  8. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms. [25] [26]

  9. Numba - Wikipedia

    en.wikipedia.org/wiki/Numba

    Numba can compile Python functions to GPU code. Initially two backends are available: Nvidia CUDA, see numba.pydata.org /numba-doc /dev /cuda; AMD ROCm HSA, see numba.pydata.org /numba-doc /dev /roc; Since release 0.56.4, [2] AMD ROCm HSA has been officially moved to unmaintained status and a separate repository stub has been created for it.