What is a GPU (Graphics Processing Unit)?

In addition to CPUs, there is another processing unit, the GPU (graphics processing unit), that helps execute other computing tasks such as graphics and video rendering. 

In this blog, you'll learn more about how the GPU aids the CPU by taking on multiple intricate tasks at the same time, avoiding overload and increasing a system's overall efficiency.

What is a GPU, and what is its purpose?

Invented by NVIDIA in 1999, the GPU is a processor that is made up of many smaller and more specialized cores than a CPU. 

They are used in a wide range of applications, including graphics and video rendering. GPUs are also becoming increasingly popular for artificial intelligence applications. 

The GPU was originally designed to accelerate the rendering of 3D graphics. Over time, they became more flexible and programmable, enhancing their capabilities.

Other developers began to tap the power of GPUs to dramatically accelerate traditional workloads in high-performance computing, deep learning, and more. 

A GPU delivers massive performance when a complex task can be divided up and processed across its many cores. This allows for parallel processing, or the completion of multiple tasks simultaneously. 

Some other key manufacturers of GPUs include Intel and AMD (Radeon). 

Where is the GPU located?

On most computers, the GPU is located on a graphics card or embedded on the motherboard on PCIe slots or riser cards.

(A graphics card is an expansion card for your computer than renders images to the display. These contain GPUs, and they can also be plugged into PCIe slots.) 

In certain CPUs, they are embedded on the CPU die itself. This is called integrated graphics. (More on that later.)

How do the CPU and GPU relate? 

CPUs and GPUs have a lot in common. They are both silicon-based microprocessors and critical computing engines that handle data.

However, they have different architectures and are built for different purposes. 

How they work together

The GPU evolved as a complement to its close cousin, the CPU. They work together to increase the throughput of data and the number of concurrent calculations within an application. 

While CPUs have continued to deliver performance increases though architectural innovations, faster clock speeds, and the addition of cores, GPUs are specifically designed to accelerate computer graphics workloads. 

How they are different 

A CPU is a generalized processor that can handle a few tasks at a time in rapid succession, also known as serial processing. In contrast, a GPU can handle many tasks at the same time, also known as parallel processing. 

The CPU is suited to a wide variety of workloads, especially those for which latency or per-core performance are important.

Operating as a powerful execution engine, the CPU focuses its smaller number of cores on individual tasks and on getting things done quickly, making it uniquely well-equipped for jobs ranging from serial computing to running databases. 

GPUs began as specialized ASICs (application-specific integrated circuits) developed to accelerate specific 3D rendering tasks. 

Over time, GPUs became more programmable and 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 the different types of GPUs?

There are two types of GPUs: integrated and dedicated (discrete). 

Integrated GPUs 

The majority of GPUs on the market fall under this category. They are also referred to as IGPs, or integrated graphics processors. 

An integrated GPU is one that is already built onto the same chip as a CPU. This allows for thinner and lighter systems, reduced power consumption, and lower system costs, also known as SWaP-C optimization. 

Dedicated (discrete) GPUs 

Dedicated, or discrete, GPUs are processors that are completely separate from the CPU and have their own dedicated memory. 

These types of GPUs are better suited for resource-intensive applications with extensive performance demands. However, they add processing power at the cost of additional energy consumption and heat creation. 

Dedicated GPUs generally require dedicated cooling for maximum performance. 

Trenton Systems and GPUs

At Trenton, our USA-made solutions support NVIDIA GPUs and next-gen Intel CPUs to accelerate AI/ML/DL workloads and big data analytics at the edge to deliver immediate, actionable insights in real-time, increasing situational awareness and shortening response times. 

Equipped with the latest cybersecurity technologies, our processors protect sensitive data and enhance security workflows to detect and address threats at the board and chip level. 

Our 3U BAM, for example, supports NVIDIA Tensor Core T4 GPUs and Intel Xeon Ice Lake SP CPUs, enhancing deep learning training and inferencing in edge computing environments. 

With PCIe 4.0 slots, the 3U BAM also offers the fastest industry-standard speeds currently available on the market for large and demanding workloads. 

Through inferencing and parallel processing, we provide customized hardware and software solutions for enhanced computing and data conversion, providing needed insights to make critical decisions in the field.