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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.
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.
When it was first introduced, the name was an acronym for Compute Unified Device Architecture, [3] but Nvidia later dropped the common use of the acronym and now rarely expands it. [4] 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. [5]
The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().
The release of the new AMDGPU kernel module and stack was announced on the dri-devel mailing list in April 2015. [28] Although AMDGPU only officially supports GCN 1.2 and later graphics cards, [29] experimental support for GCN 1.0 and 1.1 graphics cards (which are only officially supported by the Radeon driver) may be enabled via a kernel ...
Photo of James Clerk Maxwell, eponym of architecture. Maxwell is the codename for a GPU microarchitecture developed by Nvidia as the successor to the Kepler microarchitecture. . The Maxwell architecture was introduced in later models of the GeForce 700 series and is also used in the GeForce 800M series, GeForce 900 series, and Quadro Mxxx series, as well as some Jetson produ
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GPU virtualization is used in various applications such as desktop virtualization, [1] cloud gaming [2] and computational science (e.g. hydrodynamics simulations). [3] GPU virtualization implementations generally involve one or more of the following techniques: device emulation, API remoting, fixed pass-through and mediated pass-through.