The world is changing and developing every second, and with every moment, more and more information is created. As a result, computer systems accumulate and process vast amounts of data, which not every server can technically handle. Powerful graphics cards, or GPU cards, come to the rescue. A dedicated server with GPU performs well in rendering, streaming, and parallel computing — in short, it’s suitable for solving high-performance tasks. As a result, it’s not surprising that hosting servers based on GPU cards is extremely popular today.
Using GPU Servers for Supercomputing
At the dawn of the development of computer technology, all tasks were handled by the CPU, or the central processor. The tasks included calculations, audio transmission, video card request processing, and screen image output. There was no question of any multitasking: simultaneous running of two programs slowed down the PC until it froze.
Computer systems were developing, and some integrated and discrete video cards appeared. The tasks that the video cards had to solve were also becoming more complicated. As a result, video cards received their own processor — GPU. A graphics processor (aka a video processor and a graphics accelerator) is engaged in parallel computations of the same type related to graphics, as the name implies. So, thanks to the GPU, static (drawings, photos, diagrams, etc.) and dynamic (video, 3D animation, video, etc.) objects in high resolution are clearly displayed on the screen.
CPU vs. GPU
The most vital difference between a CPU and a GPU is in the way of streaming operations, which is due to the functional features of processors. So, a CPU performs operations only sequentially (one after another). Urgent tasks with high priority can be implemented, but they will also have to queue up. Each subsequent step is performed after the completion of the previous one and is based on the results obtained previously. Therefore, if an error occurs at one of the stages, the program crashes – and your PC stops working.
Modern processors are multicore. Each of the cores processes information sequentially within a single thread. In other words, different tasks are performed simultaneously in various threads (while the execution of tasks in each thread is still sequential) – that’s why it turns out to achieve multitasking.
The GPU architecture is such that there are many cores in the processor, and they are combined into blocks. At the same time, the cores work on a completely different principle than in the CPU: all the operations are performed in parallel. The GPU solves any tasks simultaneously in several threads. So, errors in one thread don’t lead to the program crash. Thanks to this fact, GPUs easily provide high (eight times higher than the CPU) performance supercomputing.
The CPU and GPU ways to access the memory and interact with it are also strikingly different. The GPU doesn’t require extensive memory – it’s writing data to the video card and then reading it. They are separate processes that take time and resources. However, most likely, this problem can be solved in the near future — new and faster methods of GPU interaction with video memory are being actively developed.
Dedicated GPU Servers
To complete high-performance supercomputing, you have to rent a GPU. The GPU architecture allows you to increase the speed of information processing since several simultaneous operations are performed per clock cycle. The advantage of using a dedicated server is that they can be combined into a single fault-tolerant cluster and used to create a productive infrastructure.
For example, renting a GPU dedicated server with NVIDIA GEFORCE 1080 TI solves the problems of accelerating graphical data processing. It’s used in the field of development, video games, and research projects where large-scale computing and various media resources are very significant.
In general, it’s possible to point out the following advantages of using GPU servers:
Fast video rendering
- The quick development of computer games
- Calculations in the framework of scientific research (for example, in the field of molecular chemistry, mathematics, and probability theory)
- Statistical calculations and development of predictive models
- Training of artificial intelligence and neural networks
- Cryptography and cryptanalysis
- Visualization, including 3D modeling and design
- Analysis and processing of big data
On the other hand, if you don’t have to work with high-performance supercomputing, you can buy a 1gbps dedicated server. It’s suitable to operate with a small amount of data. Hosting dedicated 1gbps servers is quite popular among small companies and organizations. It saves you a lot of money because the rent costs less.
The use of video cards for various kinds of computing has opened a new stage in the IT era of unprecedented speed, power, and reliability. That’s why hosting GPU servers is so in demand. A lot of companies offer equipment with GPUs. When choosing a hosting provider, pay attention not only to what it offers and prices but also to the reputation of the company and customer reviews. You should pay attention to your brand’s needs and choose the best dedicated server. Taking into consideration the amount of work, you’ll be able to make the right choice.