GPU computing


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Related to GPU computing: CUDA

GPU computing

Performing computations on a graphics processing unit (GPU). The term may refer to graphics rendering on a GPU, the original purpose of the chip, or to non-graphics processing for computation-intensive tasks in science and engineering. See GPU.
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"The high performance eBOX671-521-FL was manufactured for AI inference, GPU computing, machine learning and image processing.
"Our GPU computing platform is enjoying rapid adoption in artificial intelligence, cloud computing, gaming, and autonomous vehicles.
By providing the eminent power-efficient performance to fertile your multi-purpose projects, RCS-9000F GTX1080 GPU Computing System makes itself come to the fore.
His research interests include mobile computing, GPU computing, and multimedia network.
Plus, you can parallelize processing and even use GPU computing, especially for the benefit of machine learning, data science, and predictive analytics, going under the moniker of "Microsoft Machine Learning Services."
Its exceptional processing, low power, and compact size make the A176 Cyclone ideal for embedded deep learning, computer vision, graphics, and GPU computing applications, especially in harsh environments.
Catarinucci, "Introduction to GPU computing and CUOA programming: a case study on FOTO," IEEE Antennas and Propagation Magazine, vol.
Radeon ProRender (formerly previewed as AMD FireRender) is part of AMD's GPUOpen initiative which provides developers with open-source technology to leverage GPU computing. Radeon ProRender is a state-of-the-art unbiased path tracing renderer and physically-based material system.
Dogpu class directly using the GPGPU program Dogpu.cuh and Dogpu.cu, where Dogpu.cu is devoted to memory allocation routines, their memory for configuration and the data transfer functions do not belong to any one class order; and then that Dogpu kernel GPU computing is the core of the program, called directly by Dogpu.cu the program, Dogpu class must be achieved by Dogpu.cu GPGPU purpose of Dogpu_kernel.cu make calls.
The company says that APIs like Vulkan enable more realistic 3D graphic effects and more efficient GPU computing.
Barlas presents students, academics, researchers, and professionals working in a variety of contexts with an examination of multicore CPU programming and massively parallel of many-core GPU computing. The author has organized the main body of his text in eight chapters devoted to multicore and parallel program design, threads in shared-memory programming, OpenNP in shared-memory programming, distributed memory programming, GPU programming, the Thrust template library, load balancing, and a variety of other related subjects.