Its advances in graphics processing unit--or GPU--computing with the Cuda parallel architecture in its Tesla and Fermi-based GeForce graphics cards have made massively parallel computing ...

Parallel computing is the fundamental concept that, along with advanced semiconductors, has ushered in the generative-AI boom.

We explain what is GPU Computing, advantages of GPU and how it is used? How is GPU (Graphics Processing Unit) different from CPU.

At Nvidia's GPU Technology Conference, CEO and co-founder Jen-Hsun Huang offered a glimpse of the company's GPU roadmap and touted the growth of its CUDA parallel computing architecture.

Unlike CPUs, GPUs contain thousands of small cores that can process multiple tasks simultaneously, making them highly efficient for parallel computing.

GPU-based sorting algorithms have emerged as a crucial area of research due to their ability to harness the immense parallel processing power inherent in modern graphics processing units.

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on its own GPUs (graphics processing units).

NCAR was able to speed up the Weather Research & Forecasting Model by 20% by parallelizing one component of the model with NVIDIA CUDA software and a many-core GPU Computing Processor called Tesla.