What is a GPU server? How to choose correctly?

When it comes to video cards, it is estimated that more than 90% of people believe that this is a game tool. Are high-performance video cards just for games? At present, many companies have realized the advantages of GPU large-scale parallel computing and started to use powerful multi GPU servers to conduct research in various directions. In addition to bringing huge benefits to the company, the research results have also begun to be applied in our daily life.

What is a GPU server?

GPU server is a fast, stable and flexible computing service based on GPU, which is applied to video encoding and decoding, deep learning, scientific computing and other scenarios.

What is the function of GPU server?

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

A simple way to understand the difference between GPUs and CPUs is to compare how they handle tasks. The CPU consists of several cores optimized for sequential serial processing, while the GPU has a large-scale parallel computing architecture composed of thousands of smaller and more efficient cores designed for simultaneous multitasking.

Main application scenarios of GPU server

Massive computing processing

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

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

• Originally, dozens of CPU servers were required to work together on the cluster, which can be completed by a single GPU server.

Deep learning model

GPU server can be used as a platform for in-depth learning training:

1. The GPU server can directly accelerate computing services, and can also directly connect with the outside world.

2. The GPU server and the ECS are used together, and the ECS provides the computing platform for the main GPU ECS.

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

How to select a GPU server correctly?

When selecting a GPU server, you must first consider the business requirements to select the appropriate GPU model. In HPC high-performance computing, you also need to choose according to the precision. For example, some high-performance computing requires double precision. If P40 or P4 is not appropriate, you can only use V100 or P100; At the same time, there will also be requirements for video storage capacity. For example, computing applications for petroleum or petrochemical exploration have high requirements for video storage; There are also requirements for bus standards, so the selection of GPU model depends on the business requirements.

When the GPU model is selected, consider which GPU server to use. At this time, we need to consider the following situations:

First, we need to select T4 or P4 and other corresponding servers according to the quantity of edge server rental, and also consider the server use scenarios, such as railway station checkpoints, airport checkpoints or public security checkpoints; The V100 server may be required for the central side to do the Conference, and the throughput, usage scenarios, quantity, etc. need to be considered.

Second, it is necessary to consider customers' own users and IT operation and maintenance capabilities. For large companies such as BAT, their own operational capabilities are relatively strong, so they will choose a generic PCI-e server; For some customers with less strong IT operation and maintenance capabilities, they pay more attention to numbers and data annotation. We call them data scientists, and the criteria for selecting GPU servers will be different.

Third, we need to consider the value of supporting software and services.

Fourth, consider the maturity and engineering efficiency of the overall GPU cluster system. For example, the GPU integrated supercomputer like DGX has a very mature system that drives Dockers from the bottom operating system to other parts that are fixed and optimized. At this time, the efficiency is relatively high.

As a domestic brand server provider, the server online GPU rack server has 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 is that it can run application code by CPU while processing computing intensive tasks of large-scale parallel architecture by GPU.

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Copyright notice: This is the original article of CSDN blogger "Finovy Cloud"

Original link: https://blog.csdn.net/finovycloud/article/details/124585997