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  2. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    Note: CUDA SDK 10.2 is the last official release for macOS, as support will not be available for macOS in newer releases. CUDA Compute Capability by version with associated GPU semiconductors and GPU card models (separated by their various application areas):

  3. 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.

  4. 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 ...

  5. NVIDIA Accelerates Google Quantum AI Processor Design With ...

    lite.aol.com/tech/story/0022/20241118/9275296.htm

    The simulation techniques provided by CUDA-Q mean noisy simulations that would have taken a week can now run in minutes. The software powering these accelerated dynamic simulations will be publicly available in the CUDA-Q platform, allowing quantum hardware engineers to rapidly scale their system designs.

  6. 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.

  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]

  8. Thread block (CUDA programming) - Wikipedia

    en.wikipedia.org/wiki/Thread_block_(CUDA...

    The number of threads in a block is limited, but grids can be used for computations that require a large number of thread blocks to operate in parallel and to use all available multiprocessors. CUDA is a parallel computing platform and programming model that higher level languages can use to exploit parallelism.

  9. Nvidia NVENC - Wikipedia

    en.wikipedia.org/wiki/Nvidia_NVENC

    These features rely on CUDA cores for hardware acceleration. SDK 7 supports two forms of adaptive quantization; Spatial AQ (H.264 and HEVC) and Temporal AQ (H.264 only). Nvidia's consumer-grade (GeForce) cards and some of its lower-end professional Quadro cards are restricted to three simultaneous encoding jobs. Its higher-end Quadro cards do ...