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  2. Nvidia CUDA Compiler - Wikipedia

    en.wikipedia.org/wiki/Nvidia_CUDA_Compiler

    CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source). 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]

  4. 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]

  5. Parallel Thread Execution - Wikipedia

    en.wikipedia.org/wiki/Parallel_Thread_Execution

    The Nvidia CUDA Compiler (NVCC) translates code written in CUDA, a C++-like language, into PTX instructions (an assembly language represented as American Standard Code for Information Interchange text), and the graphics driver contains a compiler which translates PTX instructions into executable binary code, [2] which can run on the processing ...

  6. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    [7] [8] [9] The initial version was released under the Apache License 2.0 in 2015. [1] [10] Google released an updated version, TensorFlow 2.0, in September 2019. [11] TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java, [12] facilitating its use in a range of applications in many sectors.

  7. Comparison of numerical-analysis software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_numerical...

    C, Java, C#, Fortran, Python 1970 many components Not free Proprietary: General purpose numerical analysis library. Math.NET Numerics: C. Rüegg, M. Cuda, et al. C#, F#, C, PowerShell 2009 4.7.0, November 2018 Free MIT/X11: General purpose numerical analysis and statistics library for the .NET framework and Mono, with optional support for ...

  8. General-purpose computing on graphics processing units

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

    The dominant proprietary framework is Nvidia CUDA. [13] Nvidia launched CUDA in 2006, a software development kit (SDK) and application programming interface (API) that allows using the programming language C to code algorithms for execution on GeForce 8 series and later GPUs. ROCm, launched in 2016

  9. Graphics processing unit - Wikipedia

    en.wikipedia.org/wiki/Graphics_processing_unit

    Components of a GPU. A graphics processing unit (GPU) is a specialized electronic circuit initially designed for digital image processing and to accelerate computer graphics, being present either as a discrete video card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles.

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