The superior parallel processing power of GPUs explained

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, engineering, and enterprise applications. Pioneered in 2007 by NVIDIA, GPUs now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world.


How Applications Accelerate with GPUs

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.

How GPU Acceleration Works


GPU-accelerated computing leverages the many compute cores in the Quadro graphics processing unit (GPU) to accelerate highly 'parallel' calculations that tend to be pervasive in professional applications, leaving the sequential calculations to the CPU. This means a Quadro GPU is useful far beyond just 3D graphics. Examples of where Quadro GPUs can significantly accelerate compute intensive applications include rendering effects in video editing, color-grading, GPU accelerated ray-tracing and rendering, volumetric reconstruction in medical imaging, fluid simulations, and FEA analysis.

GPUs have thousands of cores to process parallel workloads efficiently


NVIDIA Quadro GPUs support the full range of parallel programming models, such as such as CUDA and OpenCL, which application developers harness to super-charge their application performance. NVIDIA developed CUDA which is widely deployed for its superior performance, flexibility, and support through thousands of applications and published research papers and supported by an installed base of over 375 million CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers. More importantly for professionals, CUDA accelerates the modern workflow in applications from vendors such as Adobe, Autodesk, Dassault Systemes, RED Digital Cinema, Blackmagic Design, and ANSYS. Get the full list of applications that take advantage of GPU accelerated computing.

Learn more about CUDA development and GPU architectures.