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  2. Nvidia NVDEC - Wikipedia

    en.wikipedia.org/wiki/Nvidia_NVDEC

    Nvidia NVDEC (formerly known as NVCUVID [1]) is a feature in its graphics cards that performs video decoding, offloading this compute-intensive task from the CPU. [2] NVDEC is a successor of PureVideo and is available in Kepler and later Nvidia GPUs. It is accompanied by NVENC for video encoding in Nvidia's Video Codec SDK. [2]

  3. Nvidia NVENC - Wikipedia

    en.wikipedia.org/wiki/Nvidia_NVENC

    Nvidia NVENC (short for Nvidia Encoder) [1] is a feature in Nvidia graphics cards that performs video encoding, offloading this compute-intensive task from the CPU to a dedicated part of the GPU. It was introduced with the Kepler -based GeForce 600 series in March 2012 (GT 610, GT620 and GT630 is Fermi Architecture).

  4. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    In computing, CUDA is a proprietary [2] 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.

  5. bfloat16 floating-point format - Wikipedia

    en.wikipedia.org/wiki/Bfloat16_floating-point_format

    Later on, when it becomes the input of matrix multiplication units, the conversion can have various rounding mechanisms depending on the hardware platforms. For example, for Google TPU, the rounding scheme in the conversion is round-to-nearest-even; [ 17 ] ARM uses the non-IEEE Round-to-Odd mode; [ 18 ] for NVIDIA, it supports converting float ...

  6. 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.

  7. General-purpose computing on graphics processing units

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

    OpenCL is actively supported on Intel, AMD, Nvidia, and ARM platforms. The Khronos Group has also standardised and implemented SYCL, a higher-level programming model for OpenCL as a single-source domain specific embedded language based on pure C++11. The dominant proprietary framework is Nvidia CUDA. [13]

  8. Mesa (computer graphics) - Wikipedia

    en.wikipedia.org/wiki/Mesa_(computer_graphics)

    Mesa maintains a support matrix with the status of the current OpenGL conformance [6] [7] visualized at mesamatrix.net. Mesa 10 complies with OpenGL 3.3 for Intel, AMD/ATI, and Nvidia GPU hardware. Mesa 11 was announced with some drivers being OpenGL 4.1 compliant. [8] Mesa 12 contains OpenGL 4.2 and 4.3 and Intel Vulkan 1.0 support.

  9. Convolutional code - Wikipedia

    en.wikipedia.org/wiki/Convolutional_code

    To convolutionally encode data, start with k memory registers, each holding one input bit.Unless otherwise specified, all memory registers start with a value of 0. The encoder has n modulo-2 adders (a modulo 2 adder can be implemented with a single Boolean XOR gate, where the logic is: 0+0 = 0, 0+1 = 1, 1+0 = 1, 1+1 = 0), and n generator polynomials — one for each adder (see figure below).