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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.
Device Dependent X (DDX), another 2D graphics device driver for X.Org Server; The DRM is kernel-specific. A VESA driver is generally available for any operating system. The VESA driver supports most graphics cards without acceleration and at display resolutions limited to a set programmed in the Video BIOS by the manufacturer. [15]
PyTorch has also been developing support for other GPU platforms, for example, AMD's ROCm [27] and Apple's Metal Framework. [28] PyTorch supports various sub-types of Tensors. [29] Note that the term "tensor" here does not carry the same meaning as tensor in mathematics or physics.
AMD Software (formerly known as Radeon Software) is a device driver and utility software package for AMD's Radeon graphics cards and APUs. Its graphical user interface is built with Qt [ 6 ] and is compatible with 64-bit Windows and Linux distributions .
AMDgpu is an open source device driver for the Linux operating system developed by AMD to support its Radeon lineup of graphics cards (GPUs). It was announced in 2014 as the successor to the previous radeon device driver as part of AMD's new "unified" driver strategy, [3] and was released on April 20, 2015.
Advanced Micro Devices (AMD) joins the newly-launched PyTorch foundation to create a sustainable ecosystem of open-source projects with PyTorch and drive the growing adoption of AI.
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.
In computing, CUDA is a proprietary [1] 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.