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The knowledge points of this article include: the role of GPU server, the application scenario of GPU server and the choice of GPU server. I believe you have a certain understanding of GPU server after reading the complete article.

GPU server is a fast, stable and flexible computing service based on GPU for video codec, deep learning, scientific computing and other scenarios.

What is the purpose of the GPU server?

GPU accelerated computing can provide extraordinary application performance, transferring the workload of the computationally intensive part of the application to GPU, while CPU still runs the rest of the program code. From the user's point of view, the running speed of the application is significantly faster.

An easy way to understand the difference between GPU and CPU is to compare how they handle tasks. CPU consists of several cores optimized for sequential serial processing, while GPU has a massively parallel computing architecture of thousands of smaller, more efficient cores designed to handle multiple tasks at the same time.

Main Application scenarios of GPU Server

Massive computing processing

The super computing function of GPU server can be applied to massive data processing, such as search, big data recommendation, intelligent input method, etc.

The amount of data that originally needs to be completed in a few days can be calculated in a few hours by using a GPU server.

It originally requires dozens of CPU servers to work together in a computing cluster, which can be completed with a single GPU server.

Deep learning model

GPU server can be used as a platform for deep learning and training:

The 1.GPU server can directly accelerate computing services and communicate with the outside world directly.

2.GPU server and CVM are used together, and CVM provides a computing platform for GPU CVM.

3. Object storage COS can provide cloud storage services with large amounts of data for GPU servers.

How to choose the GPU server correctly?

When selecting a GPU server, you should first consider the business requirements to select the appropriate GPU model. In HPC high-performance computing, it also needs to be selected according to accuracy. For example, some high-performance computing requires double precision, so if P40 or P4 is not appropriate, only V100 or P100 can be used; at the same time, there will also be requirements for video memory capacity, such as petroleum or petrochemical exploration computing applications have higher requirements for video memory; and some have requirements for bus standards, so the choice of GPU model depends on business requirements.

When the GPU model is selected, consider what kind of GPU server to use. At this point, we need to consider the following situations:

First, when leasing edge servers, you need to select corresponding servers such as T4 or P4 according to the quantity, and also consider the usage scenarios of servers, such as railway station bayonet, airport bayonet or public security bayonet; V100 servers may be needed for Inference at the central end, taking into account throughput, usage scenarios, quantity, and so on.

Second, customers need to consider their own users and IT OPS capabilities. For large companies like BAT, their own operational capabilities are relatively strong, so they will choose a general PCI-e server; while for some customers with less strong IT OPS capabilities, they pay more attention to numbers and data tagging. We call this kind of data scientists, and the criteria for choosing GPU servers will be different.

Third, the value of supporting software and services needs to be considered.

Fourth, it is necessary to consider the maturity and engineering efficiency of the overall GPU cluster system, such as the GPU integrated supercomputer such as DGX, which is very mature from the bottom operating system driver Docker to other parts are fixed and optimized, so the efficiency is relatively high.

As a domestic brand server manufacturer, specially controlled GPU rack servers have large-scale parallel processing capability and unparalleled flexibility. It is mainly used to provide sufficient processing power for computing-intensive applications. The advantage of GPU to accelerate computing is that it can run application code by CPU while graphics processing unit (GPU) handles computing-intensive tasks of massively parallel architecture. Special control GPU server is an ideal choice for medical imaging, broadcasting and video transcoding market.

After reading the above, do you have any further understanding of the GPU server? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel. Thank you for reading.

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