Why should you invest in a dedicated GPU server?
Are you dreaming about a new dedicated server? If you're like most business leaders, graphics processing units, or GPUs, you may not be the first hardware add-ons that come to mind, but they certainly warrant your attention.
GPU dedicated server implementations have several benefits over CPU-only counterparts. Here are some reasons for the switch to GPGPU, or general-purpose computing on graphics processing units.
The Benefits of GPU Offloading
If your server doesn't produce the graphics that your end-users see, why does it need a GPU? Manufacturers build GPUs for fast 3-D processing, accurate floating-point arithmetic, and error-free number crunching. While they usually run at slower clock speeds, they have thousands of cores that allow thousands of threads to be executed simultaneously.
You Get to Save Your CPU for the Big Stuff
Running computationally intensive tasks on a CPU will bind the entire system together. Unloading some of this work to a GPU is a great way to free up resources and retain consistent efficiency.
Interestingly, you can only give your GPU the most demanding workloads while the CPU manages the key sequential processes. Such GPGPU strategies are vital to providing better services to end-users who experience accelerated results.
Big Data Thrives in Parallel Environments
Many of the Big Data activities that generate business value include repetitively conducting the same operations. The wealth of cores available in GPU server hosting enables you to do this kind of work by breaking it up between processors to crunch through bulky data sets faster.
Improved Power Consumption
You don't need to be an eco-conscious organization to profit from energy-efficient computing. GPU-equipped systems that use less energy to perform the same tasks put lower demands on the supplies that power them. In specific instances, the GPU will only have the same data processing capability for 400 servers with a CPU.
Several modern software packages support GPGPU acceleration. Some even allow you to parallelize your existing code by adding hints that tell the compiler to download the GPU. Of course, certain aspects of the applications can need to be streamlined, but when parallel computing is so easy to use, there is 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 significantly from the GPGPU. GPU dedicated server devices will feed the creation of algorithms to large quantities of data in parallel. This functionality makes it much easier to teach the program how to identify the trends and patterns you're interested in analyzing.