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  2. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

  3. General-purpose computing on graphics processing units

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

    Alea GPU also provides a simplified GPU programming model based on GPU parallel-for and parallel aggregate using delegates and automatic memory management. [22] MATLAB supports GPGPU acceleration using the Parallel Computing Toolbox and MATLAB Distributed Computing Server, [23] and third-party packages like Jacket.

  4. AVX-512 - Wikipedia

    en.wikipedia.org/wiki/AVX-512

    Numenta touts their "highly sparse" [50] neural network technology, which they say obviates the need for GPUs as their algorithms run on CPUs with AVX-512. [51] They claim a ten times speedup relative to A100 largely because their algorithms reduce the size of the neural network, while maintaining accuracy , by techniques such as the Sparse ...

  5. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow includes an “eager execution” mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. [35] Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. [35]

  6. 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.

  7. PhysX - Wikipedia

    en.wikipedia.org/wiki/PhysX

    Nvidia started enabling PhysX hardware acceleration on its line of GeForce graphics cards [7] and eventually dropped support for Ageia PPUs. [8] PhysX SDK 3.0 was released in May 2011 and represented a significant rewrite of the SDK, bringing improvements such as more efficient multithreading and a unified code base for all supported platforms. [2]

  8. Tensor Processing Unit - Wikipedia

    en.wikipedia.org/wiki/Tensor_Processing_Unit

    In January 2019, Google made the Edge TPU available to developers with a line of products under the Coral brand. The Edge TPU is capable of 4 trillion operations per second with 2 W of electrical power. [44] The product offerings include a single-board computer (SBC), a system on module (SoM), a USB accessory, a mini PCI-e card, and an M.2 card.

  9. Command-line interface - Wikipedia

    en.wikipedia.org/wiki/Command-line_interface

    An MS-DOS command line, illustrating parsing into command and arguments. A command-line argument or parameter is an item of information provided to a program when it is started. [23] A program can have many command-line arguments that identify sources or destinations of information, or that alter the operation of the program.