CUDA (Compute Unified Device Architecture)

CUDA (Compute Unified Device Architecture)

Utilization of GPU and CPU to accelerate deep learning, analytics and engineering applications in C programming by Nvidia

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Edits on 29 Jan, 2018
Jude Gomila
Jude Gomila edited on 29 Jan, 2018
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Compute Unified Device Architecture (CUDA) is a computingcomputing platform and programming model. CUDA is Nvidia's GPU-accelerated computing is the only C programming environment that uses the power of the graphics processing unit (GPU) enabling faster and more cost-efficient parallel computations. is the utilization of GPU together with a central processing unit (CPU) for the purpose of accelerating deep learning, analytics and engineering applications.

Edits on 27 Jan, 2018
Melanie Manipula"Changed GPU accelerated computing to CUDA. There is a different page for GPU computing. CUDA is a particular GPU computing by Nvidia."
Melanie Manipula edited on 27 Jan, 2018
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Nvidia GPU-accelerated computing CUDA (Compute Unified Device Architecture)

Utilization of GPU and CPU to accelerate deep learning, analytics and engineering applications in C programming by Nvidia

Article

Compute Unified Device Architecture (CUDA) is a computing platform and programming model. CUDA is Nvidia's GPU-accelerated computing is the utilizationonly C programming environment that uses the power of athe graphics processing unit (GPU) enabling faster and more cost-efficient parallel computations. is the utilization of GPU together with a central processing unit (CPU) for the purpose of accelerating deep learning, analytics and engineering applications.

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CUDA GPU-accelerated computing was starteddeveloped in 2007 by NVIDIA. The GPU accelerators now support energy-efficient data centers in government laboratories, universities and small-and-medium enterprises around the world.

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CUDA GPU-accelerated computing unloads compute-intensive portions of the application to the GPU, while the remaining of the code still runs on the CPU. It accelerates applications in platforms ranging from artificial intelligence to cars, drones and robots.

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Melanie Manipula
Melanie Manipula edited on 27 Jan, 2018
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GPU acceleration Nvidia GPU-accelerated computing

Utilization of GPU and CPU to accelerate deep learning, analytics and engineering applications by Nvidia

Article

Nvidia GPU-accelerated computing is the utilization of a graphics processing unit (GPU) together with a central processing unit (CPU) for the purpose of accelerating deep learning, analytics and engineering applications.

GPU-accelerated computing was started in 2007 by NVIDIA. The GPU accelerators now support energy-efficient data centers in government laboratories, universities and small-and-medium enterprises around the world.

GPU-accelerated computing unloads compute-intensive portions of the application to the GPU, while the remaining of the code still runs on the CPU. It accelerates applications in platforms ranging from artificial intelligence to cars, drones and robots.

Related Topics
Edits on 26 Jan, 2018
Melanie Manipula"Initial topic creation"
Melanie Manipula created this topic on 26 Jan, 2018
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 GPU acceleration

Utilization of GPU and CPU to accelerate deep learning, analytics and engineering applications in C programming by Nvidia

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