GPU Computing

GPU Computing is an industry standard offered by almost every manufacturer. But what is it? GPU Computing is mainly used in very compute-intensive processes. The demanding parts of the application are allocated to the GPU, while all other calculations remain on the CPU. Unlike CPUs, GPUs have thousands of cores designed for parallel data processing. In combination with the serial processing of the few processing units on the CPU, applications can be implemented much faster. The prerequisite is that the calculations can be easily parallelized and broken down into relatively simple, uniform subtasks.  
 

Hardware for GPU Computing must meet many requirements. The server in which the GPUs are installed must be very robust, because the power consumption and waste heat of the GPUs are often many times higher than with standard systems. In addition, the CPUs used must be powerful and of high quality to control the GPUs and, when necessary, provide corresponding performance, for example on the storage side. 

Research and development are classic areas of application for GPU computing. Here, engineers, technicians and scientists have been benefiting from the power of the highly parallelized processor architecture of GPUs for years. Indeed, GPU computing is the fastest way to run the methods and algorithms used in nearly every branch of science to solve numerical problems or for simulations. GPU computing has also become indispensable for engineers and materials scientists – for instance when calculating the strength and load-bearing capacity of industrial goods, such as engine housings, wings and bridge piers. This technology can accelerate the development cycles for products of all kinds many times over. Other technical and scientific areas of application include bioinformatics, image and video processing as well as medical imaging procedures such as computer tomography.  

The boom in technologies such as artificial intelligence and machine learning in recent years would be inconceivable without GPU computing. Application developers ensure that GPUs can be integrated very easily and rely on the computing power of graphics cards. At the same time, manufacturers like NVIDIA and AMD are now even developing special GPUs whose primary task is no longer graphics output, but which are used purely for the parallelized processing of large amounts of data, such as the Tesla GPUs from NVIDIA and their successors – some of which have over 5,000 processors in one unit.