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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.
SIMT is intended to limit instruction fetching overhead, [4] i.e. the latency that comes with memory access, and is used in modern GPUs (such as those of Nvidia and AMD) in combination with 'latency hiding' to enable high-performance execution despite considerable latency in memory-access operations. This is where the processor is ...
Intel Iris Graphics and Intel Iris Pro Graphics are the IGP series introduced in 2013 with some models of Haswell processors as the high-performance versions of HD Graphics. Iris Pro Graphics was the first in the series to incorporate embedded DRAM. [5] Since 2016 Intel refers to the technology as Intel Iris Plus Graphics with the release of ...
AMD Accelerated Processing Unit (APU), formerly known as Fusion, is a series of 64-bit microprocessors from Advanced Micro Devices (AMD), combining a general-purpose AMD64 central processing unit and 3D integrated graphics processing unit (IGPU) on a single die.
Also known as: discrete graphics cards. Unlike integrated graphics, dedicated graphics cards have much more processing units and have its own RAM with much higher memory bandwidth. In some cases, a dedicated graphics chip can be integrated onto the motherboards, B150-GP104 for example. Regardless of the fact that the graphics chip is integrated ...
Nvidia Optimus is a computer GPU switching technology created by Nvidia which, depending on the resource load generated by client software applications, will seamlessly switch between two graphics adapters within a computer system in order to provide either maximum performance or minimum power draw from the system's graphics rendering hardware.
A GPU cluster is a computer cluster in which each node is equipped with a graphics processing unit (GPU). By harnessing the computational power of modern GPUs via general-purpose computing on graphics processing units (GPGPU), very fast calculations can be performed with a GPU cluster. Titan, the first supercomputer to use GPUs
This technique achieves 96–100% of native performance [3] and high fidelity, [1] but the acceleration provided by the GPU cannot be shared between multiple virtual machines. As such, it has the lowest consolidation ratio and the highest cost, as each graphics-accelerated virtual machine requires an additional physical GPU.
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