GPUs vs. CPUs: What’s the Difference?

Artificial intelligence has quickly seen widespread adoption across various industries and applications. The key to AI’s rapid development and adoption cycles is a technology known as graphics processing units (GPUs). GPUs deliver the computing power AI and high-performance computing applications need to perform optimally. While traditional CPUs are suited for a wide range of tasks, GPUs are designed to accelerate compute-intensive applications.

However, GPUs place a greater load on the data center’s electrical infrastructure and generate a significant amount of heat. Data center operators must consider these impacts when deploying systems equipped with GPUs.

What Is a CPU?

A CPU is a silicon chip comprising billions of microscopic transistors. A CPU’s core is its physical processor that reads and executes instructions from software. While early CPUs had just one core, many now have eight or more. The more cores, the greater the performance — although there are tradeoffs. 

A thread is a virtual sequence of instructions executed by a CPU. Many applications today are multithreaded, which means they divide tasks into separate threads so they can run in parallel. This increases performance and better utilizes system resources. Hyperthreading delegates processing power to multiple threads so that one core operates as two virtual cores. 

What Is a GPU and How Do They Differ From CPUs?

A GPU is also a silicon chip, but it has a different architecture from a CPU. Originally designed to accelerate the rendering of graphics, GPUs use a technique called parallel processing to perform many tasks concurrently. Multiple cores handle different parts of the same task.

GPUs have thousands of cores that are smaller and less powerful than those in CPUs. They are suited to performing similar tasks repeatedly. The large number of cores, coupled with the efficiency of parallel processing, enables a GPU to perform calculations much faster than a CPU. That makes GPUs the better choice for compute-intensive applications. CPUs are still needed to coordinate the system’s operation and perform general-purpose tasks.

CPU vs. GPU Differences in Application:

• CPUs are engineered to handle small amounts of complex tasks quickly.
• GPUs are engineered to handle many simple tasks at scale.
• CPUs are better for general computing (web servers and databases), while GPUs are better for specialized tasks (like the ones listed below).

GPU Applications

NVIDIA developed the first GPU in 1999 to enhance the rendering of graphics in computer gaming. In 2007, the company began developing GPUs to accelerate compute-intensive applications. In 2012, an NVIDIA GPU helped to usher in modern advances in AI by powering the AlexNet neural network. Today, GPUs are used in a number of advanced applications. 

Graphics

GPUs are still valuable for rendering 2-D and 3-D graphics in real-time. They can also render high-definition video for media production and gaming, and provide the processing power needed for AR/VR applications.

Artificial Intelligence

GPUs can accelerate and improve the accuracy of machine learning applications such as image recognition. They are also used for training the neural networks used in deep learning. GPUs boost the performance of deep learning algorithms, enabling many real-world applications. Without GPUs, artificial intelligence would be nowhere near as advanced and robust as it is today. 

Analytics

Some analytics tasks are better suited to CPUs, but GPUs have value in developing and training analytical models. The small, efficient cores of GPUs make them ideal for processing large datasets in parallel.

Addressing the Challenges of GPUs in the Data Center

When implementing GPUs, data center operators must ensure that the power infrastructure can handle the increased demand. Rackmount PDUs, UPSs, and other components should be assessed and upgraded if needed. Because GPUs generate a lot of heat, operators should ensure that the data center has sufficient cooling capacity and that racks and cabinets are adequately ventilated. The weight of GPU-enabled systems should also be considered.

The Enconnex InfiniRack Data Center Cabinet has a static load rating of 4,000 pounds and a dynamic load rating of 3,000 pounds. The low-profile frame maximizes internal space, while airflow management features help ensure proper cooling. With near-infinite configuration options, InfiniRack provides the versatility to support today’s dynamic data center operations. Contact one of our data center infrastructure specialists to learn more.