What is GPU Computing? | High-Performance Computing | NVIDIA ...
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GPU computing
is the use of a GPU (graphics processing unit) together with a CPU to
accelerate general-purpose scientific and engineering applications.
This is all I was able to quote. If you want to know the answer click on word button above.
The idea I get is by combining a CPU with (a good Graphics Card) things can be sped up in amazing ways. So, this might be how Smartphones might be eventually sped up too by combining a refined and powerful and miniature graphics card with a smartphones CPU. However, doing this also likely would cause the operating systems to change as well and likely you would also want at least 1 terrabyte of information storage on your smartphone too at that point.
WHAT IS GPU ACCELERATED COMPUTING?
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.
Hundreds of industry-leading applications are already GPU-accelerated. Find out if the applications you use are GPU-accelerated by looking in our application catalog.
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.
CPU VERSUS GPU
A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU consists of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.
GPUs have thousands of cores to process parallel workloads efficiently
Check out the video clip below for an entertaining CPU versus GPU.
Hundreds of industry-leading applications are already GPU-accelerated. Find out if the applications you use are GPU-accelerated by looking in our application catalog.
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.
WHAT IS GPU ACCELERATED COMPUTING?
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.
Hundreds of industry-leading applications are already GPU-accelerated. Find out if the applications you use are GPU-accelerated by looking in our application catalog.
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.
CPU VERSUS GPU
A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU consists of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.
GPUs have thousands of cores to process parallel workloads efficiently
Check out the video clip below for an entertaining CPU versus GPU.
Hundreds of industry-leading applications are already GPU-accelerated. Find out if the applications you use are GPU-accelerated by looking in our application catalog.
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.
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