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  2. 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]

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    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, [17] which supersedes the beta released February 14, 2008. [18] CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most ...

  4. 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]

  5. 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.

  6. Meson (software) - Wikipedia

    en.wikipedia.org/wiki/Meson_(software)

    Meson is free and open-source software under the Apache License 2.0. [4] Meson is written in Python and runs on Unix-like (including Linux and macOS), Windows and other operating systems. It supports building C, C++, C#, CUDA, Objective-C, D, Fortran, Java, Rust, and Vala. [5] It handles dependencies via a mechanism named Wrap.

  7. FEniCS Project - Wikipedia

    en.wikipedia.org/wiki/FEniCS_Project

    The FEniCS Project is a collection of free and open-source software components with the common goal to enable automated solution of differential equations.The components provide scientific computing tools for working with computational meshes, finite-element variational formulations of ordinary and partial differential equations, and numerical linear algebra.

  8. Comparison of linear algebra libraries - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_linear...

    Free GPL: General purpose numerical analysis library. Includes some support for linear algebra. IMSL Numerical Libraries: Rogue Wave Software: C, Java, C#, Fortran, Python 1970 many components Non-free Proprietary General purpose numerical analysis library. LAPACK [7] [8] Fortran 1992 3.12.0 / 11.2023 Free 3-clause BSD

  9. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).