Joshua Resnick - Fotolia
What does GPU hardware do in the data center?
Once part of the gaming industry, GPUs are gaining traction in data center infrastructures. Learn why the power of GPUs makes them fit virtualization and data-heavy applications.
A graphics processing unit can perform a range of complex mathematical processes far more efficiently than an ordinary central processing unit.
Graphics processing units (GPUs) can enhance performance for data center applications that require complex math functions and large data sets, such as parallel processing, SQL database calculation, image recognition, machine learning and big data analysis.
Chip designers developed the GPUs to process graphics algorithms in the gaming industry because central processing units (CPUs) weren't equipped to render 3D images on a 2D display and to render special effects. The idea was to install GPU hardware as a specialized chip -- often deployed on an expansion card such as a PCI Express card -- to offload graphics from an application. The GPU performs the rendering and applies the required effects to craft each frame of the image and then sends the image to a display attached directly to a port on the GPU card.
GPUs, however, can also handle much of the complex math needed for non-gaming applications. GPU hardware can perform 2.5 times faster than a CPU. This means the application can potentially use the GPU to perform double the work or to deliver a calculation in a fraction of the time compared to a general-purpose CPU.
The only caveat is that the application must have the proper code to support GPU hardware. Today, software vendors embed GPU code in tools for deep learning, container orchestration, and cluster management and monitoring. Not all software supports GPU processing; admins should check legacy, proprietary and mainframe applications before using GPUs.
GPU hardware works well as workload accelerators in the data center. For example, admins can use GPUs in virtual desktop infrastructure setups to support graphics and rendering tasks for virtual desktops.
Admins can also deploy GPUs in big data and scientific computing scenarios to run weather models, find correlations in huge data sets, develop machine learning applications and perform facial recognition from video images.
