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Written by Olin Coles and NVIDIA   
Monday, 16 June 2008
Table of Contents: Page Index
Compute Unified Device Architecture
Parallel Computing with GeForce
GPU vs. CPU Architecture
GPU Computing Performance

GPU Computing Performance

How do GPUs perform in real world applications?

GPUs excel in highly parallel applications. Speedups between 10× and 100× have been observed in real-world applications.

Video Encoding

For video encoding, a 110-second clip encodes in 21 seconds on the GeForce GTX 280. The same clip takes 231 seconds on the fastest CPU.


[email protected]

[email protected], the distributed-computing protein-folding application from Stanford University, runs more than 100× faster on the GPU than on the fastest CPU. Protein folding is measured in nanoseconds per day, or how many nanoseconds of the protein's life can be simulated in a day's worth of computing time. A GeForce GTX 280 can fold at 590 ns/day (up from the 511 ns/day discussed at Editor's Day in May 2008), compared to 4 ns/day on a CPU or 100 ns/day on the Playstation 3.

These results are based on the same protein and equivalent work units. At the time of this writing, a beta release of the GPU [email protected] client is available for Windows Vista users exclusively from Benchmark Reviews.


GPU Physics

Physics simulations are inherently parallel and run very well on the GPU. The table below shows common problems like cloth, soft bodies and fluid simulation. The GPU is on average is 11× faster than a quad core CPU on a preliminary implementation of the PhysX engine on the GPU.


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# cudahamid 2010-09-26 04:03
can i program gtx 260 with visual studio ?
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# RE: cudaOlin Coles 2010-09-26 08:04
Yes. The GeForce GTX 260 is fully compatible with CUDA, and will work with Visual Studio.
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