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http://blogs.nvidia.com/ntersect ... pus-says-intel.html
06/23/2010: “GPUs Are Only Up To 14 Times Faster than CPUs” says Intel
By Andy Keane, posted Jun 23 2010 at 06:00:00 AM
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/ ... 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/cont ... 0e58a313d95581cfd40π=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/cud ... l#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.c ... mp;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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发表于 2010-6-23 21:17
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