How is parallel computing achieved using CUDA C?

CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

What is CUDA library?

A key role in modern AI: the NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. No longer is it something just for the high-performance computing (HPC) community. The benefits of CUDA are moving mainstream.

Is OpenCL better than CUDA for deep learning?

While OpenCL sounds attractive because of its generality, it hasn’t performed as well as CUDA on Nvidia GPUs, and many deep learning frameworks either don’t support it or support it only as an afterthought once their CUDA support has been released.

How can you accelerate deep learning with a GPU?

You can accelerate deep learning and other compute-intensive apps by taking advantage of CUDA and the parallel processing power of GPUs. CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units).

How does the performance of a CUDA framework differ from framework?

Where the performance tends to differ from framework to framework is in how well they scale to multiple GPUs and multiple nodes. The CUDA Toolkit includes libraries, debugging and optimization tools, a compiler, documentation, and a runtime library to deploy your applications.

How much faster is CUDA compared to a CPU?

As of CUDA version 9.2, using multiple P100 server GPUs, you can realize up to 50x performance improvements over CPUs. The V100 (not shown in this figure) is another 3x faster for some loads. The previous generation of server GPUs, the K80, offered 5x to 12x performance improvements over CPUs.