Apart from CUDA, NVIDIA has also introduced their Tesla line of general-purpose GPUs. These GPUs are not responsible for displaying images onto displays, rather, alongside with CUDA, they are intended for high performance computing. To be specific, NVIDIA touts the Tesla as being able to provide the same computing power as a cluster processing, but at only a tiny fraction of the space, price and power consumption.
There are many instances where CUDA-enabled Tesla GPUs have had a positive effect in industries and services where high performance computing is essential, and Geographic Information Services (GIS) is one of them.
A GIS is a system which captures, stores, analyzes and shares geographic information. It is important because city planners and businesses use it alongside with other data to do all sorts of things such as monitor water levels, track emergency calls and identify sewerage leakages. Given that it manages and tracks a geographic location, huge amounts of data are naturally involved, and operations on the GIS can take as long as 20 minutes to complete, even on a high-end system.
Fortunately, Manifold.net, a leading GIS developer saw the potential of GPGPU and proceeded to convert its software to the CUDA platform. Release 8.00, Manifold.net's latest GIS then became the first GIS in the world to make use of CUDA technology and the benefits were jaw-dropping. With a CUDA-enabled system, Manifold.net reported that calculations which used to take 20 minutes now take only 30 seconds, while those that used to take 30 seconds, are now completed in real-time. In light of such substantial improvements, Dimitri Rotow, Product Manager of Manifold.Net, even commented that CUDA technology could be the most revolutionary development in computing since the microprocessor.