Tampa, FL (PRWEB) November 07, 2016
Hivelocity, a dedicated server, cloud hosting and infrastructure services provider to customers from over 130 countries, has announced the availability of Graphic Processing Units (GPU) with most dedicated servers and private clouds. Adding a GPU to new or existing servers is done with ease through the Hivelocity cart or ticket system. Initially, Hivelocity will offer customers the ability to add up to four NVidia Tesla or PNY Quadra GPUs within a single server, with more options coming soon.
A GPU is a massively parallel-able processing unit that can have many hundreds or thousands of cores. Where a CPU’s strength is in a single or a few complex threads, a GPU can handle many streams of data at once and process them in parallel. Deep Learning, High Performance Computing, or any application requiring highly parallel processing workloads can benefit from GPU processors. As time goes on more and more applications will need to use GPU type processing. Think of a self-driving car and the many streams of data from all the sensors that need to be fed into a computer and processed instantly. By having a highly parallel capable system on board, this data is analyzed simultaneously in lieu of waiting on a CPU to perform the tasks one at a time. The shortened time in which a GPU processes this data versus a CPU could be the difference in avoiding an accident versus crashing your car.
GPUs work in conjunction with CPUs and with properly designed applications the threading of GPU required processing happens automatically. A CPU will fork out a process to the GPU workloads that a GPU handles best, while the CPU handles workloads it does best. In the end it’s a beautiful symphony of computer engineering that will pave the way for the future applications that we have just barely scratched the surface of.
A GPU can be added to most any Hivelocity dedicated server for as little as $59 per month. For more information, please visit https://hivelocity.net and open a phone or chat dialogue with a Sales Rep.