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You probably know that most central processing units (CPUs) have a single-digit number of cores. But did you know that graphics processing units (GPUs) have thousands of cores? Why do GPUs need so many cores anyway?
GPUs have so many cores because of their unique processing requirements, which necessitate doing a significant amount of work in the shortest amount of time. GPUs are often used for applications such as gaming, 3D animation, and video editing.
In this article, we’ll explain everything you need to know about GPU cores, including why they have so many cores compared to CPUs and what constitutes an impressive amount of GPU cores.
GPUs have thousands of cores to increase their parallel processing capabilities. While the actual performance of a GPU depends on a wide variety of factors, having a large number of cores guarantees that the GPU can handle a huge amount of computations simultaneously.
To further understand why GPUs have so many cores, you may need a refresher on what GPUs and GPU cores are, how they work, and how the number of cores relates to your computer’s performance.
GPUs are generally used to process resource-intensive rendering tasks in applications like video editing and graphics rendering. Since they can process data so quickly, they are perfect for getting an editing job done quickly or ensuring a game runs seamlessly.
That isn’t the only thing a GPU can accomplish with its incredible processing power. Many of them can accelerate the function of your CPU through a process known as hybrid or heterogeneous computing.
During that process, GPUs lend their parallel processing power to the CPU to improve the performance of applications. The applications are still primarily run on the CPU, but the delegation of certain computing tasks to the GPU still significantly improves the user experience.
GPU cores are a type of segmented processing unit that allows GPUs to process a huge amount of data at incredible speeds. They can accomplish this through parallel computing, where each core is responsible for processing a small segment of the data.
Since a GPU contains thousands of cores, these small segments of data quickly add up to an incredible output. This makes them ideal for applications like graphics processing, but since their introduction, they have become powerful parallel processing units too.
To explain how exactly the number of GPU cores determines your computer’s performance, let’s start with a simple example.
Let’s say your computer has to process 16 units of data for a game.
If your GPU only has one core that takes one second to process one unit of data, it will take 16 seconds to process all of the data.
If your GPU has two cores that take one second to process one unit of data, it will take 8 seconds to process all of the data, and so on.
If your GPU has eight cores that take one second to process one unit of data, it will take two seconds to process all of the data.
Of course, the number of cores in your GPU isn’t the only factor that determines performance. If your GPU has eight cores that take two seconds to process one unit of data, it will take four seconds to process all of the data.
That means even though the GPU has the same number of cores, the performance is significantly worse.
In other words, the number of cores determines how much data your graphics card is able to process simultaneously. You’ll still need to pay attention to other specifications of the graphics card to find one with the best performance. It’s also worth paying attention to the rest of the system to ensure there isn’t any inadvertent performance bottleneck too.
Other factors worth considering as you’re comparing the performance of GPUs include the following:
- Overall clock speed of the GPU: Clock speed is a particularly important factor to consider since it can make a significant difference in frame rates.
- Thermal Dissipation Power (TDP): Make sure the Thermal Dissipation Power (TDP) is one you can adequately manage with your PC’s cooling solution.
- GPU memory: Ensure that your GPU has as much memory as possible. At least 8GB is recommended for gaming and intensive applications.
Modern GPUs can have anywhere from 300 to 4,000 cores. Low-end graphics cards tend to have only a few hundred cores, while budget options range from around several hundred to a thousand. High-end graphics cards can have thousands of cores.
While the number of cores a GPU has can vary widely, the design of a GPU tends to be similar between models. Specifically, most GPUs use a batch-based processing model that groups the cores into small segments of 16 or 32. This depends on the architecture used, which is generally either Kepler or Fermi.
Once the core batches are created, the GPU assigns processing tasks to the batch for all cores to work on simultaneously. Each batch of cores works on different processing tasks simultaneously, which allows the GPU to dedicate an impressive amount of processing power to dozens or even hundreds of computations at once.
It’s worth noting that the number of cores a GPU has differs between integrated and dedicated GPUs. Since dedicated GPUs have significantly more space inside your computer, they tend to have a lot more cores. The number of cores is one of the factors that makes the performance of dedicated graphics cards so spectacular.
The number of cores in GPUs is increasing because manufacturers want to improve the parallel processing capabilities of GPUs. Just as clock speed, memory capacity, and thermal dissipation power are being improved to make GPUs better, core count is increasing to deliver a better user experience.
Of course, there’s more context to consider regarding the number of cores in GPUs. When you look at CPUs, for example, you might notice that their core count doesn’t change significantly over time, and the underlying reason is simple.
While components like CPUs are inherently linear, GPUs have an incredibly scalable design. Because they’re designed to work with calculations in a parallel structure, there is theoretically no limit to GPU core count.
When all of the GPU’s features are simultaneously improved upon to deliver better performance with each generation, the advantages of adding more cores to each new GPU become clear. It significantly improves their capabilities for rendering graphics or doing the necessary processing for machine learning and makes new GPUs much more useful to consumers.
A wide variety of demanding processes rely on GPUs for processing power. Here are some of the most common applications for powerful GPUs.
Even if you often engage in PC gaming, you may not be familiar with all the processes that make your game so enjoyable. Essentially, having a powerful GPU with thousands of cores is important because it allows your computer to seamlessly and efficiently render all of the game’s assets at a high frame rate.
Because the game won’t be sputtering and struggling to load, you’ll find that the overall experience is much smoother. The millions of calculations necessary to render action and graphics are all done simultaneously, ensuring there isn’t any noticeable lag as you play.
Considering how these games work, it’s easy to see how a quality graphics card with a lot of cores makes them run much better. Computers that have a higher parallel processing capability can deliver higher frame rates and more consistent gameplay, making them desirable for most gamers.
As 3D graphics have become popular in animation and video games, having the necessary processing power to render them has become essential — and there’s no better way to get that power than with a good GPU.
Due to the unique requirements of 3D animation, building adequately powerful computers are a must. With a strong GPU, artists can render scenes that are simultaneously realistic and fantastical in 3D graphics. They can also produce stylized graphics more efficiently than in other mediums.
One of the most essential things 3D animators need is a GPU with powerful parallel processing capabilities, as parallel cores will be the most efficient at rendering the animation.
Editing videos is an extremely resource-intensive job. This is due to the video encoding process, which is used to prepare and format video data to make it play seamlessly. The ability to encode videos efficiently is essential when you want to be productive.
Considering how video encoding works, it’s safe to say that a CPU wouldn’t be near powerful enough to encode effectively. Because CPU processing is essentially linear, encoding would take a prohibitively long time and would take up essential system resources for the whole duration.
On the other hand, GPUs can encode videos with incredible efficiency. Because GPUs are able to parallel process data with hundreds and even thousands of cores, computers built with a good graphics card can encode high-resolution videos quickly.
One of the newest ways that GPUs’ processing power is utilized is in cryptocurrency mining. Simply put, crypto mining involves dedicating the resources of the GPU cores to a central blockchain, which uses the processing power to encrypt and store cryptocurrency transaction data as ‘blocks.’
To gain cryptocurrency, GPUs are often used to process data and add a significant amount of blocks to the blockchain. Since GPUs have thousands of cores, they can contribute blocks of data much more efficiently than a comparably powerful CPU.
Finally, one of the most important applications for GPUs is machine learning. To accomplish machine learning, a system needs to process huge amounts of data in a short time — and GPUs are perfect for ensuring the system has these capabilities.
That’s why you’ll often find GPUs used for projects like artificial intelligence. This ensures that they can get through huge amounts of data and train the artificial intelligence in the blink of an eye.
A good example of how parallel processing helps machine learning is a digital voice assistant. To understand what you’re saying, the assistant needs to learn from hundreds of thousands of voice samples in different accents, tones, and clarities.
While such a feat would be nearly impossible to accomplish with the linear processing capabilities of CPUs, the thousands of cores in a GPU make the process of teaching spoken language to a digital voice assistant simple.
Your GPU should have a number of cores based on the type of work you intend to do with it. Specifically, look for a GPU with at least a medium core count for the particular series you are considering. Core count is not comparable between different GPU series, so you must use other benchmarks.
It’s worth considering how intensive the processing you need to do is before deciding on a particular GPU.
For example, if you just want to play video games on high graphics settings, many video cards on the market are tailored specifically to fit consumer budgets while still delivering incredible performance.
On the other hand, if you’re planning on doing processing-intensive professional work, you’ll want to invest in a much higher-quality GPU. It’s easier to buy a strong GPU to begin with than it is to replace it. When you have a GPU with one of the highest core counts for its series, you can trust that tasks like video encoding will be significantly sped up by the incredible parallel processing capabilities of your GPU.
If you’re familiar with the single-digit core counts of most CPUs, it might seem strange that GPUs are marketed as containing hundreds or thousands of cores. When you consider how GPUs are used and how parallel processing improves processing speed, it’s easy to see why the core count of GPUs only continues to increase with each passing year.