Understanding NVIDIA CUDA: The Basics of GPU Parallel Computing

GPU Parallel Computing,


  • Bhanu Priya

    Bhanu Priya

    Bhanu Priya is a Technical Content Writer and Digital Marketing Specialist. She's worked with 15+ tech-based & digital Marketing companies to develop branding for several brands in India and US. She's a computer science graduate and has been writing about design, creativity and technology.

Frequently Asked Questions

NVIDIA created the parallel computing platform and programming model known as CUDA® for use with graphics processing units in general computing (GPUs).

NVIDIA's parallel computing architecture, known as CUDA, allows for significant boosts in computing performance by utilizing the GPU's ability to accelerate the most time-consuming operations you execute on your PC. In fact, because they are so strong, NVIDIA CUDA cores significantly help PC gaming graphics.

It may or may not be necessary in a deep learning framework. You do not need to have CUDA installed because Pytorch includes all of the necessary libraries. On the other side, Tensorflow appears to demand it.

A system with more than one CPU can do parallel processing. It is a process when a task is handled in parallel, it means that at least two microprocessors are used simultaneously. Parallel processing is possible on systems with several CPUs, including the multi-core processors that are frequently used in modern computers.

Multiple processors or CPUs are used in parallel processing to manage different aspects of a single task. Systems can reduce the amount of time a software takes to run by spreading a task's various components over several processors.

Deep learning solutions need a lot of processing power, like what CUDA capable GPUs can provide. Many deep learning models would be more expensive and take longer to train without GPU technology, which would limit innovation. For GPU support, many other frameworks rely on CUDA, these include Caffe2, Keras, MXNet, PyTorch, Torch, and PyTorch.

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