Whether for deep learning applications, massive parallelism, intense 3D gaming, or another demanding workload, systems today are being asked to do more than ever before. A central processing unit (CPU) and a graphics processing unit (GPU) have very different roles. What are CPUs used for? What are GPUs used for? Knowing the role that each plays is important when shopping for a new computer and comparing specifications.
What Is a CPU?
Constructed from millions of transistors, the CPU can have multiple processing cores and is commonly referred to as the brain of the computer. It is essential to all modern computing systems as it executes the commands and processes needed for your computer and operating system. The CPU is also important in determining how fast programs can run, from surfing the web to building spreadsheets.
What Is a GPU?
The GPU is a processor that is made up of many smaller and more specialized cores. By working together, the cores deliver massive performance when a processing task can be divided up and processed across many cores.
What Is the Difference Between a CPU and GPU?
CPUs and GPUs have a lot in common. Both are critical computing engines. Both are silicon-based microprocessors. And both handle data. But CPUs and GPUs have different architectures and are built for different purposes.
The CPU is suited to a wide variety of workloads, especially those for which latency or per-core performance are important. A powerful execution engine, the CPU focuses its smaller number of cores on individual tasks and on getting things done quickly. This makes it uniquely well equipped for jobs ranging from serial computing to running databases.
GPUs began as specialized ASICs developed to accelerate specific 3D rendering tasks. Over time, these fixed-function engines became more programmable and more flexible. While graphics and the increasingly lifelike visuals of today’s top games remain their principal function, GPUs have evolved to become more general-purpose parallel processors as well, handling a growing range of applications.
What Are Integrated Graphics?
Integrated or shared graphics are built onto the same chip as the CPU. Certain CPUs can come with a GPU built in versus relying on a dedicated or discrete graphics. Also sometimes referred to as IGPs, or integrated graphics processors, they share memory with the CPU.
Integrated graphics processors offer several benefits. Their integration with CPUs allow them to deliver space, cost and energy efficiency benefits over dedicated graphics processors. They bring the power to handle the processing of graphics-related data and instructions for common tasks like exploring the web, streaming 4K movies, and casual gaming.
Such an approach is most often employed with devices for which a compact size and energy efficiency are important, such as laptops, tablets, smartphones, and some desktops.
Accelerating Deep Learning and AI
Today, GPUs run a growing number of workloads, such as deep learning and artificial intelligence (AI). For deep learning training with several neural network layers or on massive sets of certain data, like 2D images, a GPU or other accelerators are ideal.
Deep learning algorithms were adapted to use a GPU accelerated approach, gaining a significant boost in performance and bringing the training of several real-world problems to a feasible and viable range for the first time.
Over time, CPUs and the software libraries that run on them have evolved to become much more capable for deep learning tasks. For example, through extensive software optimizations and the addition of dedicated AI hardware, such as Intel® Deep Learning Boost (Intel® DL Boost) in the latest Intel® Xeon® Scalable Processors, CPU-based systems have enjoyed improvements in deep learning performance.
For many applications, such as high-definition-, 3D-, and non-image-based deep learning on language, text, and time-series data, CPUs shine. CPUs can support much larger memory capacities than even the best GPUs can today for complex models or deep learning applications (e.g., 2D image detection).
The combination of CPU and GPU, along with sufficient RAM, offers a great testbed for deep learning and AI.
Decades of Leadership in CPU Development
Intel has a long history in CPU innovation beginning in 1971 with the introduction of the 4004, the first commercial microprocessor completely integrated into a single chip.
Today, Intel® CPUs let you build the AI you want, where you want it, on the x86 architecture you know. From high performance Intel® Xeon® Scalable processors in the data center and cloud to power-efficient Intel® Core™ processors at the edge, Intel delivers a CPU to match any need.
The Intelligent Performance of 10th Gen Intel® Core™ Processors
Our 10th Gen Intel® Core™ processors leverage an all new CPU core architecture, all new graphics architecture, and built-in AI instructions to intelligently deliver optimized performance and experiences.
10th Gen Intel® Core™ processor-powered systems feature the latest Intel® Iris® Plus graphics engine, which represents a huge leap forward in thin and light notebooks, pushing a smoother, more detailed, and more vivid experience than Intel has ever delivered before.
Intel® Iris® Plus graphics provides integrated processor graphics for built-in deep learning inference acceleration, offering approximately 2x improved graphics performance.2 Intel® Iris® Plus graphics also delivers exceptional power efficiency.
Intel® GPU Coming Soon
Intel is now looking to bring its CPU and integrated GPU experience to the development of dedicated GPUs. Intel expects to introduce its first discrete Intel® GPU to provide a full portfolio of CPU and GPU options, equipping you with the necessary tools for your evolving computing needs.
Today, it is no longer a question of CPU vs. GPU. More than ever you need both to meet your varied computing demands. The best results are achieved when the right tool is used for the job.
Look for announcement updates for the forthcoming Intel® GPU in the months to come.