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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.
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.
Available now, version 2.0.8 CUSHAW: Parallelized short read aligner: Parallel, accurate long read aligner – gapped alignments to large genomes: 10x: T 2075, 2090, K10, K20, K20X: Yes: Available now, version 1.0.40 GPU-BLAST: Local search with fast k-tuple heuristic: Protein alignment according to blastp, multi CPU threads: 3–4x: T 2075 ...
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 ...
Torch is used by the Facebook AI Research Group, [8] IBM, [9] Yandex [10] and the Idiap Research Institute. [11] Torch has been extended for use on Android [12] [better source needed] and iOS. [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14]
Mesa, also called Mesa3D and The Mesa 3D Graphics Library, is an open source implementation of OpenGL, Vulkan, and other graphics API specifications. Mesa translates these specifications to vendor-specific graphics hardware drivers.
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.
[9] [10] Some of the HSA-specific features implemented in the hardware need to be supported by the operating system kernel and specific device drivers. For example, support for AMD Radeon and AMD FirePro graphics cards, and APUs based on Graphics Core Next (GCN), was merged into version 3.19 of the Linux kernel mainline, released on 8 February ...