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Hardware acceleration is the use of computer hardware designed to perform specific functions more efficiently when compared to software running on a general-purpose central processing unit (CPU). Any transformation of data that can be calculated in software running on a generic CPU can also be calculated in custom-made hardware, or in some mix ...
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.
[2] [3] The efficiency aim was achieved through the use of a unified GPU clock, simplified static scheduling of instruction and higher emphasis on performance per watt. [4] By abandoning the shader clock found in their previous GPU designs, efficiency is increased, even though it requires additional cores to achieve higher levels of performance.
Real-time hardware accelerated ray tracing is a new feature for RDNA 2 which is handled by a dedicated ray accelerator inside each CU. [10] Ray tracing on RDNA 2 relies on the more open DirectX Raytracing protocol rather than the Nvidia RTX protocol.
Alea GPU also provides a simplified GPU programming model based on GPU parallel-for and parallel aggregate using delegates and automatic memory management. [22] MATLAB supports GPGPU acceleration using the Parallel Computing Toolbox and MATLAB Distributed Computing Server, [23] and third-party packages like Jacket.
Hardware-accelerated GPU scheduling: masked as an additional option in the system settings, when enabled offloads high-frequency tasks to a dedicated GPU-based scheduling processor, reducing CPU scheduling overhead. Requires ad-hoc hardware and driver support. [61] Sampler Feedback, allowing a finer tune of the resources usage in a scene. [62]
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.
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. [1]