Graphics Processing Units (GPUs) are now pivotal in high-performance computing, offering substantial computational throughput through inherently parallel architectures. Modern research in GPU ...

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

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

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.

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.