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NVIDIA GPU Tech Conference keynote - Nvidia CEO Jen-Hsun Huang


Posted by Shane Dickson (Oregon Slacker), 5 days ago
The Keynote from the Nvidia CEO started out with a fine display of 3d Video Renderings, including glass shattering images, metal balls being broke apart with imapcts to the ground, and a final display of following 4 light beams through a 3D landscape  of caverns and crevices.

The 2nd part of the keynote display started with a never before seen demo of HAWX 2, showing the huge expanse of landscapes that the Nvidia GPU architecture is capable of rendering, even in 3D without any loss of playability.  Also showed some interesting footage of volumetric simulations of procedural tesselation by the Sr. VP of Content & Technology, Tony Tamasi, one being sprayed with gas and the other interacting with the gas through use of a fan, the detail and imagery of shadows and interactions are not possible thorugh a simple artists rendering, realtime footage and interactions with the environments including flow, shadows and lightings and other environmental factors is truly possible with real time rendering.  Endless City was a 3D demo showing 1.3 billions polygons per second being rendered with over 1000 light sources. The last demonstration was that of a lighthouse with rolling waves being displayed from a multi grid height field simulation, and 1/2 million particles doing the spray and foam, and finally interactive objects/debris that was a coupled rigid body simulation that when interacted with would actually dynamically change the waves as they interacted.  This was touted as next gen gaming technologies and as being run on only one GPU.   

Main Announcements: Nvidia has partnered with the #1 solution providers in the world to help make the world a better place.
  • Cuda X86 PGI - Compiler to deploy Cuda programmed applications on any computer/server in the world, despite CPU/GPU, Parallel or Multiprocessor.
  •  Matlab’s (#1 in Numerical Calculations) announcement of a Cuda Accelerated Parallel  Programming Toolbox. Matlab used by over 1 million researches world will support Cuda across a cluster of GPU’s with up to a 40x increase over CPU clusters.
  • Multi GPU  now supported by Amber 11 (#1 in Moleculer Dynamics, stands for Assisted Model Building w/ Energy Refinement) thanks to work by UCSD Professor Ross Walker and his team.  This tools used for biomoleculer simulations including protein forcefields, show scaling of Nvidiia  MultiGPU solutions outperforming the Supercomputer Kraken with its 192- quad cores CPU’s only processing 46 ns/Day but a 8 Fermi GPU solution, able to process 52 ns/day (based on JAC benchmark) proving the ideas that Supercomputer processing does not require such a huge space and be both readily available and affordable.
  • Leading provider of Simulated Driven Product Solutions, Ansys has announced its beta testing results while using an Nvidia Solution. gaining up to 2x faster rendering in house without any optimization work having even been started yet. While this doesn’t seem like a huge gain when you consider things like it taking 60+ CPU hours to render the simulation of an airplane breaking system, this can now be done in just 30+ hours via a GPU solution. although time is an important factor in design, what this really equates to is the ability to generate as many solutions as possible given a deadline for a project.  This in turn results in being able to run as many simulations as possible to determine the best solution possible, even when a customer may change the design specs, maybe even one you might not even have got a chance to preview giving older more time consuming CPU solutions alone.

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Comments
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  #1 Sep 23, 2010 21:55:52 5 days ago

melonakos
1 Comments

At AccelerEyes, we are happy that MathWorks has finally turned the corner on accepting GPU technology, something we’ve been pushing them to do since 2007.

We are also excited, because this is great news for the GPU computing ecosystem, to see one of the technical computing giants validate the approach we’ve been delivering with Jacket.

Finally, we are thrilled, because we are confident that anyone attempting to use GPU computing via MATLAB directly will end up becoming a Jacket programmer (for instance, try indexing into a matrix or running a convolution with R2010b). To see how Jacket stacks up against the alternative, see:

* http://www.accelereyes.com/products/compare

Jacket will always offer the best in GPU computing technology. Without Jacket’s runtime technology, no MATLAB GPU computing attempt will be able to come close to competing in performance benefits on real applications. It is one thing to get a few functions to run on the GPU. But getting full applications to run fast on the GPU is an entirely different matter.

We invite you to check it out for yourself by visiting, http://www.accelereyes.com

 

John Melonakos

CEO

AccelerEyes


 
 
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