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  2. OpenCV - Wikipedia

    en.wikipedia.org/wiki/OpenCV

    OpenCV runs on the desktop operating systems: Windows, Linux, macOS, FreeBSD, NetBSD and OpenBSD as well as mobile operating systems: Android, iOS, Maemo, [19] BlackBerry 10 and QNX. [20] The user can get official releases from SourceForge or take the latest sources from GitHub. [21] OpenCV uses CMake.

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA 9.0–9.2 comes with these other components: CUTLASS 1.0 – custom linear algebra algorithms, NVIDIA Video Decoder was deprecated in CUDA 9.2; it is now available in NVIDIA Video Codec SDK; CUDA 10 comes with these other components: nvJPEG – Hybrid (CPU and GPU) JPEG processing; CUDA 11.0–11.8 comes with these other components: [20 ...

  4. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). [18] TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [citation needed]

  5. oneAPI (compute acceleration) - Wikipedia

    en.wikipedia.org/wiki/OneAPI_(compute_acceleration)

    oneAPI is an open standard, adopted by Intel, [1] for a unified application programming interface (API) intended to be used across different computing accelerator (coprocessor) architectures, including GPUs, AI accelerators and field-programmable gate arrays.

  6. Vulkan - Wikipedia

    en.wikipedia.org/wiki/Vulkan

    Vulkan targets high-performance real-time 3D-graphics applications, such as video games and interactive media, and highly parallelized computing.Vulkan is intended to offer higher performance and more efficient CPU and GPU usage compared to the older OpenGL and Direct3D 11 APIs.

  7. rCUDA - Wikipedia

    en.wikipedia.org/wiki/RCUDA

    rCUDA, which stands for Remote CUDA, is a type of middleware software framework for remote GPU virtualization. Fully compatible with the CUDA application programming interface ( API ), it allows the allocation of one or more CUDA-enabled GPUs to a single application.

  8. 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.

  9. GPUOpen - Wikipedia

    en.wikipedia.org/wiki/GPUOpen

    GitHub: OpenCL: Linux, macOS, Windows: physically-based rendering engine RadeonRays SDK (formerly FireRays) GitHub: DirectX 12, Vulkan: Linux 64-bit, OS X, Windows 64-bit: A high efficiency, high performance heterogeneous ray tracing intersection library for GPU and CPU or APU on any platform. RapidFire SDK GitHub: DirectX, OpenGL: Windows