GPU Parallel Processing – What You Need To Know

gpu parallel processing

What is GPU parallel processing and why it’s so important?

The whole world is changing and it’s changing very fast. 20 years ago, nobody could imagine that Artificial Intelligence would take over the world and would be the primary source of technology. Ray tracing, highperformance computing and many other research programs are based on it and that was not foreseen. The majority of the world’s jobs are directly or indirectly connected to AI and this is a progress everybody needs and needed. 


What is a GPU?

GPU stands for Graphics Processing Units and is that part of the computer that allows us to perform multiple tasks. It is based on a new brand technology and it made artificial intelligence, data science and many other branches of research possible. Even the COVID vaccine was made with the help of AI, so GPUs are a big part of our lives, even if we don’t know it yet. Or at least not all of us. Google Cloud is one of the biggest server GPU providers in the world, along with AWS and Azure. By server GPU, we understand the cloud that provides the GPU. It’s as if you were to use your own computer, but it’s better. 


GPU accelerated the process of machine learning, deep learning, ray tracing etc. Therefore, the power of GPUS cannot be neglected or denied. Maybe that’s why they cost so much? A really great server can cost thousands of dollars monthly, but if you’re a beginner when it comes to AI you shouldn’t bother getting a very performant server. Moreover, graphical processing units are used in rendering, too. Though they aren’t as exact as CPUs, the difference is barely noticeable. 


There are lots of GPUs creators, but the most popular ones are the NVIDIA GPUs. Because of their graphics cards and because of the technology they use to create these graphics cards, they’ve become the top distributors. And the whole world still relies on NVIDIA GPUs. For example, even Google uses on their servers such parts of computers, such as NVIDIA Tesla V100, P40 etc. On the other hand, the CPUs are still an alternative to GPUs, but let’s see to what extent.


Speaking of the devil, CPU stands for Central Processing Unit. Even so, it is not able to process as many parallel programs as a GPU. That’s why GPU parallel processing is so popular. When it comes to rendering or AI, go for a GPU computer if you want to speed up the process, as the tasks that are given to the PC will be massively parallelized and you will have more time to develop other projects, too.

Auxilio’s NVIDIA GPU Parallel Processing

GPU architectures are widely known between AI lovers but not only. Auxilio offers a solution to your gaming, AI, rendering and not only problems, by offering you a trial that starts at only 1$. If you are interested in getting a GPU card server at low costs, which offers almost the same quality as Google Cloud or AWS, then go for Auxilio. 

See how GPU parallel processing can help you: 

  1. Parallel computing on graphics processing is easier when you have a supercomputer.
  2. Image processing becomes faster.
  3. Computation on Graphics Processing Unit is simply better.

Even though the majority of technology runs on the CPU architecture, we should remind you that GPUs are better.

So start your journey with Auxilio and start programming GPUs within a few seconds and clicks!