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  2. 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] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  3. 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]

  4. 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.

  5. 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.

  6. Nvidia DGX - Wikipedia

    en.wikipedia.org/wiki/Nvidia_DGX

    Announced March 2024, GB200 NVL72 connects 36 Grace Neoverse V2 72-core CPUs and 72 B100 GPUs in a rack-scale design. The GB200 NVL72 is a liquid-cooled, rack-scale solution that boasts a 72-GPU NVLink domain that acts as a single massive GPU . Nvidia DGX GB200 offers 13.5 TB HBM3e of shared memory with linear scalability for giant AI models ...

  7. 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.

  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. Nvidia Jetson - Wikipedia

    en.wikipedia.org/wiki/Nvidia_Jetson

    384-core Nvidia Volta architecture GPU with 48 Tensor cores 6-core Nvidia Carmel ARMv8.2 64-bit CPU 6MB L2 + 4MB L3 8 GiB 10–20W 2023 Jetson Orin Nano [20] 20–40 TOPS from 512-core Nvidia Ampere architecture GPU with 16 Tensor cores 6-core ARM Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 4–8 GiB 7–10 W 2023 Jetson Orin NX 70–100 TOPS