What is a GPU?
GPU stands for Graphics Processing Unit and are also often referred to as graphics cards. A GPU is a processor that has been specially designed to handle intensive graphics rendering tasks. Common uses of graphics cards are in embedded systems, mobile phones, personal computers, workstations, and game consoles.
The term GPU for this component became widespread in the 1990s when it was coined by Nvidia. Nvidia’s GeForce range of GPUs was initially popular as they enabled the evolution of hardware acceleration, programmable shading, and stream processing.
GPU for Gaming?
Many consider a graphics card to be a gamer’s best friend. This is because a good graphics card is vital if you want to play the latest gaming titles. As newer games become more advanced in their graphics capabilities, they will require an appropriate graphics card.
What this means is that if you are trying to play a graphics-intensive game, but don’t have a suitable graphics card, you, unfortunately, will not be able to play it, or your game will lag and graphics quality will be reduced. Most games on the market today will have a specified minimum requirement for a graphics card to help you choose the best component for your gaming PC.
These recommended requirements are there to help you get the best experience when playing the game. For example, some requirements are provided to ensure you can access key gaming features such as ray tracing.
Why use a GPU for Mining?
You can also use a GPU for mining Bitcoin and other digital currencies, collectively known as cryptocurrencies. New cryptocurrencies are coming online daily, and these can sometimes be mined with your average computer, or one which has a stand-alone GPU card installed.
A standard GPU - such as a Radeon HD 5970 executing 3,200 32-bit instructions per clock is 800 times faster than a CPU that executes only 4 32-bit instructions per clock. The increased speed offered by a GPU makes them highly suitable for cryptocurrency mining, as such tasks require higher efficiency in performing similar kinds of repetitive computations.
GPU for Deep Learning
There are some companies, such as Nvidia, that are at the forefront of developing artificial intelligence (AI) technologies. Deep learning is the subset of machine learning that drives many AI applications, such as ones that improve automation. To run deep learning, you will need a good amount of compute power. GPUs are optimal for tasks such as training AI and deep learning models as they’re able to process multiple computations simultaneously.
A GPU has a number of cores, which allows for improved computation of multiple parallel processes. The memory bandwidth of a GPU makes it the most suitable for handling the computations involved in deep learning which processes enormous amounts of data.
What is the Difference Between a CPU and a GPU?
We like to think of a CPU (Central Processing Unit) as the brain of a computer, and a GPU as its creative side, helping to render graphical user interfaces into visually appealing designs and icons rather than reams of black and white lines. A GPU tends to contain more logical cores than a CPU (ALUs, or Arithmetic Logic Units, these carry out arithmetic and logic operations).
The main difference between GPUs and CPUs is that a GPU allocates proportionally more transistors to ALUs and less to flow control and cache as compared to a CPU.
A CPU is commonly used for computations that require parsing through or interpreting complex logic in code. On the other hand, a GPU is designed to handle dedicated graphical rendering tasks and accelerate geometric calculations. In short, bandwidth is one of the main reasons why a GPU is faster for computing than a CPU.
Can a GPU be used instead of a CPU?
Tasks like rendering basic objects such as an operating system’s desktop environment can usually be handled by a CPU with its built-in basic graphics processing functionalities. However, should you have a more strenuous workload requiring extra horsepower, you will need a dedicated GPU.
With the ability to process many pieces of data simultaneously, GPUs are useful for machine learning, gaming applications and video editing, thus freeing up CPU cycles for other jobs.
Is it worth buying a GPU?
You may find it worthwhile to buy a GPU for a number of different reasons. If you are looking to use video or photo editing software, a fast GPU would be ideal. What’s more, if you are running such software on a high-res screen such as a 4K display, you will see improved performance when using a GPU. This component also improves page rendering and video streaming when using web browsers and popular streaming apps.
There are different GPU models available for both laptop and desktop computers. Pricing for a standard model starts at around $30 and rises from there, which is good news if you are on a budget because you can expect to get appreciable computing benefits from a lower-end model if you aren’t looking for a GPU for gaming.
You can either include a GPU in your device configuration at the point of purchase or you can install one on your own if you have a desktop. It is straightforward to install this component in most desktops and involves just opening the case and slotting in the card.
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