Why Is Intel’s New Graphics Processor So Important? GA S REGULAR Menu Lifewire Tech for Humans Newsletter! Search Close GO News > Computers
Why Is Intel’s New Graphics Processor So Important?
It's more than just looks
By Charlie Sorrel Charlie Sorrel Senior Tech Reporter Charlie Sorrel has been writing about technology, and its effects on society and the planet, for 13 years. lifewire's editorial guidelines Published on November 4, 2020 11:00AM EST Tweet Share Email Tweet Share Email Computers Mobile Phones Internet & Security Computers & Tablets Smart Life Home Theater & Entertainment Software & Apps Social Media Streaming Gaming Key Takeaways
GPUs are like buses: slower than sports cars, but much better at shifting a lot of numbers in parallel.GPUs are used in machine learning, medicine, image processing, and games.Intel’s Iris Xe Max is designed to make laptops more powerful for creators and AI. Acer Intel’s new Iris Xe Max Graphics Processor Unit is now showing up in laptops, and by all accounts it’s a big deal. But what is a GPU, and why is it important? Spoiler: It’s not about games, or even graphics. The CPU in your computer, the one that does the day-to-day work, is expensive, and highly specialized. A GPU, on the other hand, is really, really good at math. Specifically, they can multiply big numbers, and they can perform many, many operations in parallel. This makes them good for generating complex 3D graphics, but they are used for much more. "GPUs are great for big data, machine learning, and image processing," 3D animator David Rivera told Lifewire via instant message. "I have many colleagues who use it in medicine to get MRI results." Big Math Big Pictures
Anything that requires a lot of complicated math is perfect for offloading to the GPU. "Graphics are usually very powerful because calculating 3D video stuff is very complex," Barcelona-based computer engineer Miquel Bonastre told Lifewire via instant message. But soon, computer boffins realized these math machines could be pressed into use for all kinds of math-intensive tasks. "Now, supercomputing clusters are also being made with GPUs. They are used for scientific calculations, engineering, etc," says Bonastre. Another advantage of the GPU is that it’s easy to scale up. It’s built to run identical operations in parallel, so adding more chips (or just more cores to the chip design, making it bigger) makes everything faster. A GPU is also great for processing photographs. For example, Adobe’s Lightroom photo-editing suite can offload work to your Mac or PC’s graphics processor to "provide significant speed improvements on high-resolution displays," which includes 4K and 5K monitors. "CPUs are optimized for latency: to finish a task as fast as possible," writes AI consultant Ygor Rebouças Serpa. "GPUs are optimized for throughput: they are slow, but they operate on bulks of data at once." Serpa compares a CPU to a sports car, and a GPU to a bus. The bus is a lot slower, but it can shift a lot more people. What About Your Phone
The GPU in your phone is used to drive its super high-resolution display, and to run the graphics. That’s why the phone gets hot when you play a game—the GPU kicks in, and your phone has no fan to cool it down. On the iPhone, the GPU is used for image recognition, natural language learning, and motion analysis. That is, it processes images and video as you shoot them, and more. GPUs are great for big data, machine learning, and image processing. But that’s not all. Apple’s recent iPhones and iPads contain a "Neural Engine." This is a big chip, specially designed to carry out machine-learning tasks. It’s not a GPU, but it’s GPU-like in concept, in that it crunches hard math problems in no time at all. The latest version is, according to Apple, "capable of performing up to 11 trillion operations per second." Machine Learning
Perhaps the biggest buzzword in computing right now is "machine learning." This involves showing the computer a lot of examples, and letting the computer work out the similarities and differences. GPUs are perfect for this because they can view more examples per second. However, once that training is done, the GPU is no longer needed. Any learned algorithms can be run faster by the CPU. Now, let’s go back to Intel’s new Iris Xe Max GPU. This is designed for running in "thin-and-light laptops and [to] address a growing segment of creators who want more portability," said Intel Vice President Roger Chandler in a statement. That is, it’s meant to make power-constrained laptops better for editing video, photos, and any other GPU-intensive activity. Yes, including AI. The Iris Xe Max is designed for machine learning. Perhaps its first task will be to learn how to pronounce its own name. Was this page helpful? Thanks for letting us know! Get the Latest Tech News Delivered Every Day Subscribe Tell us why! Other Not enough details Hard to understand Submit More from Lifewire Microsoft's Surface Pro 9 Shows That the Future Is ARM, Not Intel Windows 10: Release Date, Editions, Features, and More What Is a GPU (Graphics Processing Unit)? 6 Things to Consider Before Buying a Laptop Using Graphics Cards for More Than Just 3D Graphics How to Speed up a Chromebook What Is a CPU? (Central Processing Unit) The 8 Best Desktop PCs of 2022 The 7 Best i7 Processors of 2022 AMD vs Intel: Which Processor Is Best for You? What Is Overclocking? Should You Ever Overclock Your Computer? The 9 Best Laptops for College Students of 2022 How to Check if a Computer Can Run a Game The 7 Best Processors of 2022 What is VRAM? The 6 Best 17-Inch and Larger Laptops of 2022 Newsletter Sign Up Newsletter Sign Up Newsletter Sign Up Newsletter Sign Up Newsletter Sign Up By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. Cookies Settings Accept All Cookies