Wednesday 23 June 2010

nTersect

nTersect


GeForce Gets a Home on Facebook

Posted: 23 Jun 2010 11:07 AM PDT

Greetings GeForce Community

We've been listening to your feedback online, and know that you're hungry for more access to NVIDIA and you want to be heard. Among this blog – where we publish news about all things NVIDIA - our Official Forums – where you can discuss technical issues – various Twitter Handles – sending bite-size snippets of news - and the NVIDIA Facebook fan page, we're trying to give you as many touch points as possible to share, discuss, and connect with us and the NVIDIA Community-at-large.

That being said, many have been asking us to create a dedicated Facebook Fan Page specifically for GeForce fans. We get it, you want all GeForce and gaming news all the time, and there's nothing wrong with that. So, we'd like to express our thanks to the community who's helped us get where we are today, by announcing the launch of the NVIDIA GeForce Facebook Fan page. Consider this a dedicated sandbox on Facebook for you to talk and learn about what matters most to you – Gaming with GeForce! Know that we're always reading your comments, and love to hear from you. We may not be able to address every question, but we are always listening.

Happy gaming, and we'll see you on Facebook.

GeForce on Facebook

“GPUs Are Only Up To 14 Times Faster than CPUs” says Intel

Posted: 23 Jun 2010 06:00 AM PDT

It's a rare day in the world of technology when a company you compete with stands up at an important conference and declares that your technology is *only* up to 14 times faster than theirs. In fact in all the 26 years I've been in this industry, I can't recall another time I've seen a company promote competitive benchmarks that are an order of magnitude slower.

The landmark event took place a few hours ago at the International Symposium on Computer Architecture (ISCA) in Saint-Malo, France, interestingly enough, the same event where our Chief Scientist Bill Dally is receiving the prestigious 2010 Eckert-Mauchly Award for his pioneering work in architecture for parallel computing.

At this event, Intel presented a technical paper where they showed that application kernels run up to 14 times faster on a NVIDIA GeForce GTX 280 as compared with an Intel Core i7 960. Many of you will know, this is our previous generation GPU, and we believe the codes that were run on the GTX 280 were run right out-of-the-box, without any optimization. In fact, it's actually unclear from the technical paper what codes were run and how they were compared between the GPU and CPU. It wouldn't be the first time the industry has seen Intel using these types of claims with benchmarks.

The paper is called "Debunking the 100x GPU vs CPU Myth" and it is indeed true that not *all* applications can see this kind of speed up, some just have to make do with an order of magnitude performance increase. But, 100X speed ups, and beyond, have been seen by hundreds of developers. Below are just a few examples that can be found on CUDA Zone, of other developers that have achieved speed ups of more than 100x in their applications.

Developer

Speed Up

Reference

Massachusetts

General Hospital

300x

http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-17-22-20178

University of Rochester

160x

http://cyberaide.googlecode.com/svn/trunk/papers/08-cuda-biostat/vonLaszewski-08-cuda-biostat.pdf

University of  Amsterdam

150x

http://arxiv.org/PS_cache/arxiv/pdf/0709/0709.3225v1.pdf

Harvard University

130x

http://www.springerlink.com/content/u1704254764133t5/?p=c5eead9af73340e58a313d95581cfd40&pi=49

University of Pennsylvania

130x

http://ic.ese.upenn.edu/abstracts/spice_fpl2009.html

Nanyang Tech, Singapore

130x

http://www.opticsinfobase.org/abstract.cfm?URI=oe-17-25-23147

University of  Illinois

125x

http://www.nvidia.com/object/cuda_apps_flash_new.html#state=detailsOpen;aid=c24dcc0f-c60c-45f9-8d57-588e9460a58f

Boise State

100x

http://coen.boisestate.edu/senocak/files/BSU_CUDA_Res_v5.pdf

Florida Atlantic University

100x

http://portal.acm.org/citation.cfm?id=1730836.1730839&coll=GUIDE&dl=ACM&CFID=88441459&CFTOKEN=90295264

Cambridge University

100x

http://www.wbic.cam.ac.uk/~rea1/research/AIRWC.pdf

The real myth here is that multi-core CPUs are easy for any developer to use and see performance improvements. Undergraduate students learning parallel programming at M.I.T. disputed this when they looked at the performance increase they could get from different processor types and compared this with the amount of time they needed to spend in re-writing their code. According to them, for the same investment of time as coding for a CPU, they could get more than 35x the performance from a GPU. Despite substantial investments in parallel computing tools and libraries, efficient multi-core optimization remains in the realm of experts like those Intel recruited for its analysis. In contrast, the CUDA parallel computing architecture from NVIDIA is a little over 3 years old and already hundreds of consumer, professional and scientific applications are seeing speedups ranging from 10 to 100x using NVIDIA GPUs.

At the end of the day, the key thing that matters is what the industry experts and the development community are saying and, overwhelmingly, these developers are voting by porting their applications to GPUs.

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