A graphics processing unit (GPU) allows a computer to render complex 3D images, render high-quality video games, and handle large amounts of information.
It is commonly found in gaming PCs and VSIs, but common desktop and laptop computers also include a GPU.
A GPU forgoes the instruction execution advances of the CPU for the specialized hardware acceleration of 3D graphics calculations.
GPU vs. CPU
A GPU, or graphics processing unit, is a processor that focuses on graphics-related tasks.
It has a higher performance per watt than a CPU, your computer’s main processor.
GPUs have more cores than CPUs.
The core is the part of the processor that executes instructions in parallel by performing mathematical and logical calculations – it’s like the brain of the processor.
GPUs perform faster parallel processing than CPUs because they have so many cores.
Hardware that performs fast parallel processing, like GPUs, is often used for video rendering and cryptocurrency mining because these operations require so much power to do one thing at once.
GPUs also typically have larger caches (memory reserved for frequently-used data) than CPUs.
Larger caches help speed up specific tasks and are especially useful for gaming because games require large amounts of memory and constant access to them as you play.
The last key difference between GPUs and CPUs is that GPUs generally have a higher memory bandwidth (the rate at which data can be read from or stored in memory) and more significant amounts of memory capacity (how much data can be stored).
This means that any task involving large amounts of data, such as 3D rendering, can be performed more quickly on a GPU than on a CPU since they can store and retrieve more information at once.
Difference Between a GPU and Graphic Card?
Many people are confused with the terms GPU and graphics card.
A GPU is a computing unit, while a graphics card is a circuit board that holds the GPU and video memory.
The term GPU came into existence more recently, but it has been used to refer to the processor integrated on a graphics card since its inception.
This may not be true as GPUs can also be found on motherboards and even on high-end CPUs in some cases.
GPUs are fast becoming independent chip processors like CPUs so that they can be located at various places in the computing device or system.
GPUs have microprocessors inside them to quickly process complex graphical computations to produce stunning visuals in games, movies, etc. and create stunning visual effects on photo editing software such as Adobe Photoshop CC.
What are GPUs Used For?
Companies can also use GPUs to accelerate deep learning and artificial intelligence applications.
If you enjoy cryptocurrency mining, you probably have a GPU or two chilling in the back of your computer.
Graphic designers need powerful video cards that provide the processing power for 3D rendering and advanced image editing software such as Adobe Photoshop.
Why GPU is Faster Than CPU?
But the difference between CPU and GPU is that GPU has thousands of cores that can handle thousands of threads simultaneously.
This makes it more appropriate for parallel processing, which is needed in graphic work, scientific calculations, and machine learning.
Let’s say that you have a very hectic day and you have to do many things simultaneously: get up, get dressed, cook breakfast, prepare lunch for your kids, clean the house and cook dinner.
You can look at your list and decide to execute each task one after the other (sequential computing).
Or you can divide the tasks among your family members so that each one does something at once (parallel computing).
Parallel computing will be faster because some tasks will be done simultaneously, while with sequential computing, they are done one after another.
As a gamer, you’ve undoubtedly heard of the GPU by now. So what is GPU?
A graphics processing unit (GPU) is a single-chip processor primarily used to manage and boost video and graphics performance.
Once the domain of high-end workstations and rendering farms, GPUs are now commonly found in personal computers and mobile devices.
GPUs have become necessary to accelerate computationally intense operations common in machine learning applications such as image classification and object detection.
GPUs excel at parallel processing tasks—the simultaneous execution of multiple elements of an application—by dividing workloads into smaller jobs that can be performed in parallel before aggregating them back together at the end.
What is The Use of GPUs in Data Centers?
GPUs can be used in data centers to enable AI computing and machine learning.
GPUs offer more cores, higher bandwidth, and lower latency compared to CPUs.
In addition, GPU processing is extremely power efficient compared to traditional CPU-based computing for deep learning workloads.
Many GPUs are used for rendering (also called visualization) because of GPUs’ high parallelism.
Render farms use dozens or even hundreds of graphics cards to render complex images like those found in animated films or special effects for movies.
A single image can take hours or days for your computer’s processor to create, as each pixel must be processed sequentially (in order).
However, a graphics card can process all the pixels at once, giving you a much faster turnaround time on a project.
Data scientists and developers also use GPUs for handling big data in parallel.
Today engineers use distributed clusters with hundreds of GPU nodes—each node containing multiple GPUs—to deliver high-performance computing solutions to solve complex problems like simulating weather patterns and large-scale scientific models across domains such as geophysics, quantum chemistry, astrophysics, and thermodynamics.
Cryptocurrency mining is another example of how massively parallel processing power within GPUs enables an entirely new class of applications that run on decentralized networks without centralized server farms like Amazon Web Services (AWS)
Decentralized blockchains require thousands of computers worldwide with specialized hardware that are constantly running advanced algorithms against cryptographic puzzles, which allow these systems to be decentralized while still providing network consistency and transaction confirmation services such as the Bitcoin network does today with its Proof-of-Work blockchain protocol.
A GPU Mainly Does Computations
The second part of the GPU, called the execution unit (EU) takes in binary instructions from the instruction buffer and processes them.
The EU is where all of the complex calculations using data from memory are done. If you think of a CPU as a brain that does thinking, then think of a GPU as a hand that does work.
To conclude, GPUs have the capacity to do a lot more than just power your video game graphics. If you’ve ever used an application that involves complex calculations, there’s a good chance your GPU has helped out with that.
The next time you use your computer, pay close attention to the work it’s doing for you—you might be surprised to see how often you’re using its graphical powers!