- 2200 Geniuses and a Self-Driving
Car - After the success of last
year's GPU Technology
Conference, we were
pretty excited to host our 2nd event in September this year. Our attendee numbers
grew more than 50%, well above average for a technical conference, and submissions
from eager CUDA developers wanting to present their work grew nearly 400%. In fact,
we had so many that we doubled the number of sessions at the conference to 280, all
of which are online
for your viewing and listening pleasure :)
It was pretty interesting to see the difference in the show since last year. The sheer breadth of topics covered made the show unlike any other - from astrophysics to video processing, from computational fluid dynamics to neuroscience and from energy exploration to designing autonomous cars. Tables were filled with engineers, scientists, developers, students and researchers, all sharing experiences and ideas. We'll be staying in San Jose, California for GTC 2011, and we hope to see you all there.
Here are a few of my favorite quotes from members of the press that attended:
"Absolutely one of the best - and most important conferences in the technology and advanced computing sector" - The Exascale Report
"What we are seeing here is like going from propellers to jet engines." - insideHPC
"...GTC is growing even as it specializes on just one aspect of NVIDIA's business, the CUDA platform for GPU computing. That's just one of many signals that point to an undeniable trend: the use of GPUs for non-graphics computation is on the rise, led largely by NVIDIA's efforts." - Tech Report
"NVIDIA's GTC is a blast. The demos, keynotes, exhibits, technical papers, and emerging companies' presentations are first class, interesting and informative. Well worth the price of admission. There was no heavy product messaging, no call to action to buy something other than the idea that parallel processing is here and it's important-and by our observations it was mission accomplished." - Tech Watch
- Turbocharged Tools - This year we saw GPU-enabled, production releases of
some of the most important applications in the technical and scientific computing
space. ACUSIM Software launched a GPU-enabled
version of its CFD software AcuSolve, delivering double the
for its users.
Tom Lange, director of Modeling and Simulation at P&G said:
"GPU-accelerated CFD allows for more realism, helping us replace slow and expensive physical learning cycles with virtual ones. This transforms engineering analysis from the study of failure to true virtual trial and error, and design optimization."
ANSYS released performance data on its CUDA implementation of ANSYS Mechanical, revealing that CUDA helps cut turnaround times for complex simulations in half. Wolfram Research released the latest version of Mathematica, delivering for its users, in some cases, speed increases of more than 100X from within the familiar confines of the Mathematica programming environment. Check out the video here of their demo earlier this year at Siggraph. And finally, NVIDIA and Mathworks collaborated on its latest release of MATLAB 2010b, to include support for GPU acceleration for users of Parallel Computing Toolbox and MATLAB Distributed Computing Server.
- Cloudy with a chance of GPUs - this year saw the first GPU deployments to
from Peer1 in July and Amazon Web Services
(AWS) in November. Developing for the CUDA architecture of NVIDIA GPUs already
offers the lowest cost of entry for any HPC architecture, but with these new
services, you don't even need to buy the hardware yourself. Through AWS for
example, you can now get access to 2 Tesla 20-series GPUs and 2 CPUs for just $2.10
an hour. Businesses of all sizes can now run heavy duty simulations and more with
simple on-demand pricing , and no large up front capital investment.
GigaOm Pro had
to say about the announcement:
"Performance (of Amazon's Cluster Compute Instances) was high already, and the addition of GPUs just ups the octane level. According to a benchmark test by HPC cloud-resource middleman Cycle Computing, GPU Instances outperform in-house GPU clusters in certain cases."
- Lean, Mean & Green - The year ended with a bang for the Tesla business at
SC'10 in New Orleans. The final Top500 and Green500 lists of the year were announced
and Tesla had its best showing yet. Just prior to SC'10 commencing, the National
Supercomputer Center in Tianjin announced
which, with a Linpack score of 2.57 petaflops, secured the #1 spot on the list. Two other Tesla
made the Top5; the aforementioned Nebulae, and Tsubame 2.0 from Tokyo Tech.
Tsubame 2.0 was ranked at #2 in the Green500, but more notably it was the only petaflop system in the entire Top 10. Equipped with 4200 Tesla GPUs, yet consuming just 1.340 megawatts, it is, by far, the most power efficient petaflop system the world has ever seen ands an incredible achievement from Prof. Satoshi Matsuoka and his team.
NVIDIA and its customers were also recognized in a number of industry awards at the show. GPUs were highlighted in two Gordon Bell1, 2 awards. The best student paper went the way of Tokyo and Purdue Universities who collaborated on a new interface to make parallel programming on the GPU even more accessible. And perhaps most exciting, we saw some major organizations receiving honors for their work with GPUs, including , SchlumbergerCitadel Investment Group and Weta Digital .
- Some Years in Review for my Year in Review - while writing this, a couple of
other year in review articles caught my eye, and included some quotes that I thought
would make a fitting end to this recap.
HPCwire released their biggest trends of the year podcast last week and pronounced GPU Computing as the #1 Trend of the Year. They commented:
"This year, it (GPU Computing) hit the mainstream, deployed by all the major vendors."
They also added that:
"If NVIDIA hadn't been there, this wouldn't have happened. AMD was only lukewarm about this. NVIDIA put energy and money into it. They changed the trajectory of GPU computing, without a doubt. NVIDIA CUDA made this possible."
Another article recently appeared on O'Reilly Media, who produce a wealth of books, online services, magazines, research, and conferences for the technical computing community. Their summary was that GPUs coupled with CPUs is the architecture of choice for the processing of computationally heavy data
"You won't get the processing power you need at a price you want just by enabling traditional multicore CPUs; you need the dedicated computational units that GPUs provide."
We couldn't agree more :)
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