GPU Supercomputing and Digital Revolution

The GPU in a system enhances the Floating Point Operations per Second (FLOPS) and lays the foundations for GPU Supercomputing.  To begin with, the Graphic Processing Units or the GPUs are known as Socket compatible servers that come laden with advanced chip architecture. High end GPU servers are connected to a cluster of external chassis that in turn are strengthened with PCIe Express circuits. This potent combination yields itself to a network configuration of up to 32 GPUs. The Popular open-source enterprises have endorsed the GPU functionality of such complex systems and given it a thumbs-up. GPU supercomputing is poised to transform the businesses and processes of modern day technology.

GPU supercomputing makes optimum use of its CPU along with all other vital components simultaneously. As is well known, the CPU is the store-house of processes and program instructions and since its inception has been diligently carrying out the work assigned to it. In the recent past, advancements in technological developments have enabled CPUs with more capacity to process and play around with numbers. With the invention of a GPU, the load on a CPU was reduced and the GPU occupied itself with managing complicated computations with help of microprocessors. In comparison to a CPU, a GPU has far greater resolutions of up to 16 bit or 32 bit colour value per pixel. Also, at the beginning, the GPUs were conceived with a view to process 2D graphics thereby enhancing the drawing in a Graphic User Interface on a Windows platform. Later, however with the advent of 3D the GPU too was transformed into a more sophisticated specialized system. As a result, the GPUs we have today function as ‘floating point processors’ that easily handle high value geometric calculations along with texture identifying processes.

The GPUs have further enhanced their compatibility by expanding their platform and including the MPEG settings for easy viewing of videos. Some versions of these High Performance Computing (HPC) systems can directly interpret the HD signal thereby giving more freedom to the CPU to focus on program instructions.  Thus the GPU supercomputing makes best possible use of the two mighty systems. In terms of hardware, both the processing units look similar yet they are different. There are more transistors in a GPU as compared to a CPU. In some ways, the CPU still rules the domain of programming instructions whereas the GPU takes care of extra computations and graphic functionality only. GPUs in a series can be inserted into a network for best speeds.  The disk space, the Memory on the PCIe Bus, compatibility with testing of climate modeling, astrophysics, nuclear energy calculations etc., range of temperatures in which they can function, core processors, and many such parameters are crucial for a GPU supercomputing process to be useful. If you are looking to invest in such a process, it is advisable to first understand your requirements and then approach a reputed name in the business with a proven track record.

This guest post was supplied by superxpert.com, experts in GPU Supercomputing.