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ROCm [3] is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing.
PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable NVIDIA GPU. PyTorch has also been developing support for other GPU platforms, for example, AMD's ROCm [26] and Apple's Metal Framework. [27] PyTorch supports various sub-types of Tensors. [28]
In computing, CUDA (Compute Unified Device Architecture) 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.
Launch – Date of release for the GPU. Architecture – The microarchitecture used by the GPU. Fab – Fabrication process. Average feature size of components of the GPU. Transistors – Number of transistors on the die. Die size – Physical surface area of the die. Core config – The layout of the graphics pipeline, in terms of functional ...
Moore Threads Technology Co. Ltd (Chinese: 摩尔线程) is a Chinese technology company specializing in graphics processing unit (GPU) design, established in October 2020 by Zhang Jianzhong (张建中), the former global vice-president of Nvidia and general manager of Nvidia China. [1]
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.
Zen 4 is the name for a CPU microarchitecture designed by AMD, released on September 27, 2022. [4] [5] [6] It is the successor to Zen 3 and uses TSMC's N6 process for I/O dies, N5 process for CCDs, and N4 process for APUs. [7]
Many libraries support bfloat16, such as CUDA, [13] Intel oneAPI Math Kernel Library, AMD ROCm, [14] AMD Optimizing CPU Libraries, PyTorch, and TensorFlow. [10] [15] On these platforms, bfloat16 may also be used in mixed-precision arithmetic, where bfloat16 numbers may be operated on and expanded to wider data types.