You may have come across or read about the GPU in the modern computing world. If you are a gamer or do some resource-intensive work on a PC, you might already be familiar with the GPU. In this guide, we explain what is GPU computing and how is it used?
What is GPU Computing?
A graphics processor Where GPUs in short is used as a co-processor with the CPU for heavy tasks and computation.
Generally, GPUs are used to speed up or speed up memory-intensive tasks such as image or video processing, rendering, or gaming. GPU has become a necessity to perform such tasks as it has many cores and superior memory bandwidth.
Not only is the GPU used in memory-intensive tasks, but it is gradually being used to perform scientific computing work. Since 2006, it has become suitable for a wide range of computing tasks that use a lot of CPU and GPU. GPU computing has steadily grown in performance and has never slowed down in performance for computing tasks.
The GPU offloads some of the memory or compute intensive tasks from the CPU and speeds up the tasks. GPU and CPU performing intensive tasks at higher speeds is known as heterogeneous or hybrid computing.
What is GPU computing used for?
Usually, GPUs are used to speed up graphics processing tasks such as image or video editing, or gaming. GPUs have immense power to help the CPU perform intensive computing tasks and it is now used to perform such tasks. Along with GPU usage in games and content creation like video editing or graphics, it is widely used in machine learning. In machine learning and AI, GPU is mostly used to process heavy duty tasks like image recognition etc.
Read: Best Free Tools to Compare CPU and GPU on Windows Computer
What are the benefits of GPU computing?
The benefits of the GPU are beyond words. As stated earlier, the GPU has the ability to perform heavy computing tasks by offloading the intense computation from the CPU and working with it in parallel, and delivering results faster. The lag-free games we play, the extreme quality videos or images we see, the artificial intelligence services we use that deliver real-time results are the benefits of GPU computing. Simply put, GPU computing is all about speed and parallel computing. Thus, whenever there is a need to perform a computing task that requires more CPU and time, the GPU can help perform it quickly and in less time.
The main benefits of GPU are parallel computing, machine learning, multitasking, analytics, data science, bitcoin mining, etc.
What is the GPU in a computer in simple terms?
The tasks that we perform on our PC are mostly done by the CPU. Some tasks require more computer capabilities or resources, like playing heavy games or editing pictures or videos. Then the need for a GPU or graphics processing unit arises. It works with the processor in parallel and performs heavy-duty tasks, allowing the processor to focus on tasks that require less memory or computing power.
When to use the GPU in computing?
You can use GPU computing when doing heavy computing work such as machine learning or implementing artificial intelligence. Even if you don’t have machine learning or AI tasks, you can use GPU computing while playing heavy games on your computer or performing graphics processing such as image editing or rendered video. GPU computing makes programs respond faster and saves you time.
What is the difference between CPU and GPU?
Both central processing units (CPUs) and graphics processing units (GPUs) are critical computing engines. The CPU has fewer cores than the GPU, which increases the computing power of the GPU. The CPU can perform multiple tasks at once, but the time it takes is greater than the GPU.
Read: How to Check GPU Temperature in Windows.