GPU Computing, the basics:

GPU-accelerated computing is the use of a Graphics Processing Unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient data centers in government labs, universities, enterprises, and small-and-medium businesses around the world. They play a huge role in accelerating applications in platforms ranging from artificial intelligence to cars, drones, and robots.


GPU-accelerated computing offloads compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user’s perspective, applications simply run much faster.

How GPU Acceleration Works

GPU versus CPU Performance

A simple way to understand the difference between a GPU and a CPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 GPUs have thousands of cores to process parallel workloads efficiently
GPU versus GPU: Which is better?

Check out the video clip below for an entertaining GPU versus CPU comparisson

Video: Mythbusters Demo: GPU vs CPU (01:34)