A PC’s GPU (Graphics Processor Unit) is familiar term — it’s the component which enables video and sophisticated graphics, such as video games, to run on the PC. What started out as a peripheral supplementing the central processing unit (CPU) has completely repositioned itself to become the central component in high performance computing, self-driving cars, artificial intelligence, etc.
In these high-performance applications the GPU is used together with a CPU to accelerate deep learning, analytics, and engineering applications for platforms ranging from artificial intelligence to cars, drones, robots, search engines, interactive speech, video recommendations and much more.
What is the difference between GPUs and CPUs
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 with lots of cache memory that can handle a few software threads at a time by using sequential serial processing. A GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed to handle multiple tasks simultaneously. The ability of a GPU with hundreds of cores to process thousands of threads can accelerate software by 100x compared to a CPU on its own.
An analogy commonly used to explain the difference between CPU and GPU is the task of looking for a word in a book. If this task is handed to a CPU, it would start at page 1 and read it all the way to the end, because it’s a “serial” processor. It would be fast, but would take time because it has to go in order. A GPU, which is a “parallel” processor, would tear the book into a thousand pieces and read it all at the same time. Even if each individual word is read more slowly, the book may be read in its entirety quicker, because words are read simultaneously.
Because a GPU is highly optimized to perform advanced calculations such as floating-point arithmetic, matrix arithmetic, and the like, they can perform functions like video conversion and post-processing more effectively than the CPU. In addition, the GPU achieves this acceleration while being more power- and cost-efficient than a CPU.
The video demonstrates the power of GPU computing.