If your server isn’t creating the graphics that your end-users see, then why does it need a GPU? Manufacturers design GPUs for fast 3-D processing, accurate floating-point arithmetic, and error-free number crunching. Although they typically operate at slower clock speeds, they have thousands of cores that enable them to execute thousands of individual threads simultaneously.
You Get to Save Your CPU for the Big Stuff
Running computationally intensive tasks on a CPU can tie up the whole system. Offloading some of this work to a GPU is a great way to free up resources and maintain consistent performance.
Interestingly, you can just send the toughest workloads to your GPU while the CPU handles the main sequential processes. Such GPGPU strategies are critical to delivering better services that cater to end-users, who experience accelerated performance.
Big Data Thrives in Parallel Environments
Many of the Big Data tasks that create business value involve performing the same operations repetitively. The wealth of cores available in GPU server hosting lets you conduct this kind of work by splitting it up between processors to crunch through voluminous data sets at a quicker rate.
Improved Power Consumption
You don’t have to be an eco-conscious company to benefit from energy-efficient computing. GPU-equipped systems that use less energy to accomplish the same tasks place lower demands on the supplies that power them. In specific use cases, a GPU can provide the same data processing ability of 400 servers with CPU only.
Many modern software packages support GPGPU acceleration. A few even let you parallelize your existing code by including hints that tell the compiler where to offload work to the GPU. Of course, you may need to optimize certain parts of your applications, but when it’s this easy to take advantage of parallel computing, there’s no reason to hold back.
You Can Give Your Machine Learning Processes a Head Start
Tasks that rely on deep-learning and other AI training methods benefit greatly from GPGPU. GPU dedicated server devices can feed developing algorithms huge volumes of data in parallel. This capacity makes it much easier to teach your software how to recognize the trends and patterns that you’re interested in analyzing.