Beyond Gaming: High-Performance Visual
Computing and Professional Computation
With the power of CUDA technology and the new CUDA runtime for Windows
Vista, intensive computational tasks can be offloaded from the CPU to the GPU.
GeForce GTX 200 GPUs can accelerate numerous rich-media and computationally-
intensive applications such as video and audio transcoding, or running distributed
computing applications like Folding@home in the background while surfing the
web. Examples of GPU-enabled applications include the RapidHD video
transcoding application from Elemental and various video and photo editing
applications.
Many engineering, scientific, medical, and financial areas demand high-performance
computational horsepower for numerous applications.
Figure 3 shows the amazing speedups that can be achieved by using a GPU instead
of a CPU in a number of professional visual computing applications, in addition to
mainstream video transcoding. Appendix B lists references and details for these
applications.
Figure 3: Significant Speedup Using GPU
With an understanding of the GeForce GTX 200 GPU design goals and key
objectives, let’s delve deeper into its internal architecture, looking at both the
graphics and parallel processing capabilities.
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May, 2008 | TB-04044-001_v01
