GPU accelerated computing is the use of a graphics processing unit (GPU) in conjunction with a CPU to accelerate deep learning, analysis and engineering applications like 3D configurators. Developed by Nvidia in 2007, GPU accelerators are now energy-efficient data centers in government laboratories, universities, businesses, and small and medium businesses around the world. They play a major role in accelerating applications on platforms ranging from artificial intelligence to cars, drones and robots.
If you prefer visual tutorials, we recommend you watch the following video:
How GPUs accelerate software applications.
GPU-accelerated computing frees the GPU from CPU-intensive parts of the application while the remaining code is still running on the CPU. From the user`s point of view, applications simply run much faster.
GPU vs. CPU performance.
A significant difference between GPU and CPU are the processes involved in accomplishing tasks. While a CPU consists of several cores optimized for sequential and serial processing, the GPU is characterized by a massive and parallel architecture of smaller, more powerful cores designed for simultaneous processing of multiple tasks. The video below illustrates the differences between GPU and CPU:
I hope that in this article we were able to teach you the basics of GPU accererated computing.
Thank you.
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