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Accelerated Computing
Solving the World's Most Important Challenges
Accelerated Computing - Solving the World's Most Important Challenges

BRING THE POWER OF DEEP LEARNING
TO YOUR DATA

Cloud computing has revolutionized industries by democratizing the data center and completely changing the way businesses operate. Your most important assets are now in the cloud with your preferred provider. However, to fully pull insight from that data you need the right high-performance computing solution.

NVIDIA Deep Learning Software is built for maximum performance on the world’s fastest GPUs and integrates our optimized deep learning frameworks, libraries, drivers and operating system. This unified stack runs on a range of environments from TITANX or GeForce GTX 1080Ti, to DGX systems, to the cloud and is readily accessible and available 24x7x365.

GPU cloud computing is also available on-demand on all major cloud platforms.

HOW GPUs ACCELERATE SOFTWARE APPLICATIONS

GPU-accelerated computing offloads 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 much faster.

How GPU Acceleration Works
 

GPU vs CPU Performance

A simple way to understand the difference between a GPU and a CPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

GPU Vs GPU: Which is better?

Check out the video clip below for an entertaining GPU versus CPU

Check out the video clip below for an entertaining GPU versus CPU
Video: Mythbusters Demo: GPU vs CPU (01:34)

With over 400 HPC applications acceleratedincluding 9 out of top 10—all GPU users can experience dramatic throughput boost for their workloads. Find out if the applications you use are GPU-accelerated in our application catalog (PDF 1.9 MB).

GET Started TODAY

There are three basic approaches to adding GPU acceleration to your applications:
  • Dropping in GPU-optimized libraries
  • Adding compiler "hints" to auto-parallelize your code
  • Using extensions to standard languages like C and Fortran

Learning how to use GPUs with the CUDA parallel programming model is easy.

For free online classes and developer resources visit CUDA zone.

VISIT CUDA ZONE

 
 
 
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