CUDA is NVIDIA’s parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU (graphics processing unit).
With millions of CUDA-enabled GPUs sold to date, software developers, scientists and researchers are finding broad-ranging uses for CUDA, including image and video processing, computational biology and chemistry, fluid dynamics simulation, CT image reconstruction, seismic analysis, ray tracing, and much more.
Computing is evolving from “central processing” on the CPU to “co-processing” on the CPU and GPU. To enable this new computing paradigm, NVIDIA invented the CUDA parallel computing architecture that is now shipping in GeForce, ION, Quadro, and Tesla GPUs, representing a significant installed base for application developers.
In the consumer market, nearly every major consumer video application has been, or will soon be, accelerated by CUDA, including products from Elemental Technologies, MotionDSP and LoiLo, Inc.
CUDA has been enthusiastically received in the area of scientific research. For example, CUDA now accelerates AMBER, a molecular dynamics simulation program used by more than 60,000 researchers in academia and pharmaceutical companies worldwide to accelerate new drug discovery.
In the financial market, Numerix and CompatibL announced CUDA support for a new counterparty risk application and achieved an 18X speedup. Numerix is used by nearly 400 financial institutions.
An indicator of CUDA adoption is the ramp of the Tesla GPU for GPU computing. There are now more than 700 GPU clusters installed around the world at Fortune 500 companies ranging from Schlumberger and Chevron in the energy sector to BNP Paribas in banking.
And with the recent launches of Microsoft Windows 7 and Apple Snow Leopard, GPU computing is going mainstream. In these new operating systems, the GPU will not only be the graphics processor, but also a general purpose parallel processor accessible to any application.
How to install Cuda in Linux
Check the link below