Have you ever wondered: how do graphics-intensive games get rendered on a computer screen? How does an entire Minecraft universe play so smoothly on a standard computer? What’s the key to high-definition video processing and editing? The answer to all these questions lies in one word: GPU.
In this article, we take a look at what GPU is, its features, and what it is used for. Without further ado, let’s dive in!
What is a GPU?
A GPU or a Graphics Processing Unit is a computing device that is strong enough to handle large volumes of parallel processing load. It is generally used for graphics-related tasks. A GPU is a miniature computing environment in itself, with its own processing cores and memory. It is mostly used for scientific computing tasks and mathematical operations.
Graphics processing relies heavily on mathematics, as colors are nothing more than numbers in matrices. Due to the mathematical nature of graphics-processing related tasks, they can be easily carried out on a GPU.
Gaming is one of the most popular fields that use GPUs, as almost all games rely on heavy graphics to provide a captivating experience for gamers. However, GPUs are also used in more critical fields such as medical imaging, which requires high precision and fast delivery.
Types of GPU
Before we move on to discuss their features, let’s take a moment to understand the two most popular types of GPUs available in the market:
Integrated GPU
Integrated GPUs are chipsets built inside the main chipset, i.e. the motherboard of a computer system. They offer multiple benefits, as they are compact, energy-efficient, and less expensive than a discrete GPU.
However, integrated GPUs tend to perform a little lower than their dedicated counterparts. This is due to the fact that an independent ecosystem is built around dedicated GPUs, with their own cooling mechanism and isolated chipsets. An integrated GPU is the best choice for those who are looking to do basic graphics-related tasks on their computer.
Discrete GPU
A discrete GPU is sold as a separate chipset and can be connected to your system’s motherboard via a connector. These chipsets house their own cooling mechanisms such as heat vents, fans, or even liquid cooling systems, and they tend to perform better than their integrated counterparts.
However, manufacturing an independent chipset is more costly, which is why they are almost always priced higher than their integrated counterparts. A dedicated GPU is the best choice for those who are looking to get the most out of their computer systems for tasks like video processing and gaming.
GPU vs CPU
GPU vs CPU has been a cause of much confusion among computer users. Let’s take a moment to understand how they are different:
GPU (Graphics Processing Unit)
As mentioned previously, a GPU is a chipset that is wired for processing graphics-related data. Since graphics-processing is a math-heavy task, GPUs can also take up other complex arithmetic tasks.
The fundamental architecture of a GPU contains a huge number of low-capacity processing cores. These cores are where the actual calculations happen. The sheer number of these cores facilitates the process of carrying out parallel computations. Such processors offer extremely high throughput. This means that a lot of complex work gets done quite fast on these chipsets.
CPU (Central Processing Unit)
CPUs, on the other hand, are general-purpose chipsets, that can carry out a variety of tasks easily. They are the heart of a computer system; all processes, from booting to shutting down, happen via the CPU.
The architecture of a CPU is very different from that of a GPU. A CPU has a limited number of high-performing processing cores. This makes CPUs the best candidate for carrying out serial processing tasks faster. Due to the high performance of individual cores, very little time is lost between non-parallel tasks. This makes CPUs the perfectly balanced alternative for carrying out general-purpose calculations in a computer system.
Features of GPU
Let’s take a look at some of the top benefits of using GPUs:
Carrying Out Graphics-Intensive Tasks
The primary function of a GPU is to carry out graphics-intensive tasks. GPUs offer better parallel processing performance compared to traditional CPUs as they house a great number of cores as well as a considerable amount of independent memory. This makes some tasks, like editing photos and videos and running high-end games, easy as they are directed away from the main CPU.
Carrying Out Complex Calculations Fast
A processor that is capable of doing graphics-based calculations is strong enough to carry out scientific and mathematical calculations faster than the usual processors. This feature makes GPUs useful in fields like machine learning and data analytics. Vendors such as Nvidia offer support for running heavy machine learning tasks on their GPUs, which can make the process much faster than with traditional CPUs.
More Than Just Graphics
GPUs are not just good for graphics; they can be used to do other tasks as well. If an application offers support for running on a GPU, you can clear out a lot of load from your CPU, which bears the weight of running your operating system and managing your hardware. A GPU is similar to an additional set of CPU and memory; if you know how to utilize it properly, you can effectively double your system’s capabilities.
Limitations of GPU
While GPUs may seem the way to go in most complex applications, they do have some limitations. Some of the biggest ones are:
GPUs Are Costly Compared to CPUs
CPUs are way cheaper than GPUs. This is due to the fact that GPUs are programmed to be faster, and are equipped with way more assets than simply a few cores. The added load of memory and an independent cooling requirement adds considerably to the cost of these chipsets.
GPUs Require an Independent Cooling Mechanism
As mentioned in the previous point, GPUs require an additional, independent mechanism for cooling. This can either be a heat sink that dissipates the heat into the air over a large surface, a fan that drives the air into the hottest parts, or a mechanism that dissipates the heat with the help of liquids.
No matter which one you go with, you will incur additional costs, including the cost of maintenance. This clearly is not the case for standard CPUs, which traditionally require only a single fan to maintain temperature.
Not All Applications Can Use GPUs
Another considerable limitation is that not all applications support deferring their processing-intensive tasks to a GPU. This means that even if you have a powerful GPU installed in your system, you might not be able to use it to run all of your heavy applications. While this will get better in the future as more and more software organizations declare support for GPU-based processing, it certainly counts as a limitation at the moment.
What Are GPUs Used For?
A GPU can be used for a number of tasks:
- Gaming: GPUs are widely used for running high-end games. Such games require huge processing capacities, which can not be met by a standard CPU.
- Editing media: A very popular use of GPUs is editing and processing media. Because GPUs are programmed to process graphics, they can edit photos and videos much faster than CPUs.
- Training machine learning models: A growing application of GPUs is to train machine learning models. Training such models requires a huge number of iterations and mathematical calculations, which can easily be carried out in parallel by a GPU.
Conclusion
GPUs are an effective tool in processing graphics and carrying out complex calculations. They provide an excellent alternative to the traditional CPUs with limited parallel computing capabilities. GPUs have applications in a variety of fields, from gaming to scientific computing. Having a GPU in your computer system opens a whole new world of possibilities in data processing and media editing and creation, among other applications.
In this article, we explained the basics of GPUs and compared the two most popular types. We took a look at the benefits as well as the limitations of using a GPU. Finally, we ended our discussion with the most popular use cases of the chipset.
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