EVGA GTX 260 FTW Edition Reviewccokeman - July 7, 2008
Folding@Home is a distributed computing project run out of Stanford University. This project uses the spare CPU cycles (GPU Folding has been available on the red side of the fence with ATI cards for a while), as well as GPU cycles to simulate the folding of proteins. When the proteins in our bodies misfold, things can go horribly wrong and result in many diseases that are not yet curable. Examples include Alzheimer's, Mad Cow (BSE), CJD, ALS, Huntington's, Parkinson's Disease and many cancers and cancer-related syndromes. This project has been going on for some time now. With the performance increases in CPU and GPU computing technology seen in the past few years, the time to run the simulations has dramatically dropped. For more information on the F@H project, visit the F@H main page - and don't forget, Team 12772 is the one you want to fold for! While monitoring the F@H client, I was amazed at the speed at which it completed the assignments - five work units in less than two hours at stock clock speeds! Running the SMP clients took about a day to process one+ unit with a quad-core CPU. There is definitely a substantial performance increase with the CUDA technology and the GTX 260's 192 processor cores. Some things I found out while playing with the client - when the client is run in the viewer, the CPU usage skyrockets. As soon as it is minimized back to the system tray, the CPU usage drops dramatically. Because the demands to render the image back to the screen are high, the client's performance does decrease, but there's an easy fix - just minimize the client!
The processing power and processor design of the GTX 200 series GPU allows the video card to be used for things that people do not normally associate with the GPU's functionality. Using Nvidia's CUDA technology to harness this power, things such as distributed computing and video transcoding can be accomplished in much less time than it would take a high-end CPU. The Folding@Home client is just one of these examples. Elemental Technologies has a transcoding application called BadaBoom that harnesses the massive parallel computing potential of the GTX 200 series GPU. CPU usage between the BadaBoom app and the one used for testing showed that CPU usage was fairly close, but the GPU-specific BadaBoom version did the work in less than half the time it took the CPU to complete the task.
Just to see how well this works, a sample film clip of 184MB in size was transcoded first with the CPU, and then again with the GPU, and the results were pretty astonishing. The measurement is in seconds, and best quality was selected. Hey, it really does work!