WHAT IS CUDA?
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.

PARALLEL COMPUTING WITH CUDA

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.

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.

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.