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New NVIDIA Tesla GPU Smashes World Record in Scientific Computation

btarunr

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NVIDIA today unveiled the Tesla M2090 GPU, the world's fastest parallel processor for high performance computing. In addition, the Tesla M2090 GPU achieved the fastest-ever performance in a key measure of scientific computation. Equipped with 512 CUDA parallel processing cores, the Tesla M2090 GPU delivers 665 gigaflops of peak double-precision performance, enabling application acceleration by up to 10x compared to using a CPU alone.

In the latest version of AMBER 11, one of the most widely used applications for simulating behaviors of biomolecules, four Tesla M2090 GPUs coupled with four CPUs delivered record performance of 69 nanoseconds of simulation per day. The fastest AMBER performance recorded on a CPU-only supercomputer is 46 ns/day.



"This is the fastest result ever reported. With Tesla M2090 GPUs, AMBER users in university departments can obtain application performance that outstrips what is possible even with extensive supercomputer access," said Ross Walker, assistant research professor at the San Diego Computer Center, and principle contributor to the AMBER code.

The Tesla M2090 GPU will be available in servers such as the new HP ProLiant SL390 G7 4U server. As part of the SL6500 Scalable System of HP server solutions, optimized for scale-out and HPC market segments, the SL390 family was purpose built for hybrid computing environments that combine GPUs and CPUs.

The SL390 G7 4U server incorporates up to eight Tesla M2090 GPUs in a half-width 4U chassis and, with a configuration of eight GPUs to two CPUs, offers the highest GPU-to-CPU density on the market. The system is ideally suited for applications ranging from quantum chemistry and molecular dynamics to seismic processing and data analytics.

"Clients running intensive data center applications require systems that can process massive amounts of complex data quickly and efficiently," said Glenn Keels, director of marketing, Hyperscale business unit, HP. "The decade long collaboration between HP and NVIDIA has created one of the industry's fastest CPU to GPU configurations available, delivering clients the needed processing power and speed to handle the most complex scientific computations."

In addition to AMBER, the Tesla M2090 GPU is ideally suited to a wide range of GPU-accelerated HPC applications. These include molecular dynamics applications, NAMD and GROMACS, computer-aided engineering applications, ANSYS Mechanical, Altair Acusolve and Simulia Abaqus, earth science applications, WRF, HOMME and ASUCA, oil and gas applications, Paradigm Voxelgeo and Schlumberger Petrel as well as other key applications such as MATLAB, GADGET2 and GPU-BLAST.

For more information on the HP ProLiant SL390 G7 4U server, go here, and for more information on the Tesla M2090 and the whole family of M-class products, please go here.

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What is a "ns" as in "46 ns/day" ?

Edit, Nevermind... "nanoseconds of simulation per day"
 

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I am more interested in that cooler lol.
 
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Can somebody please tell me again why you can't use these to play games?
 
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Because there is no DVI or HDMI connector

i was gonna say because at the price of this thing you could probably go dual proccesor and quad SLI
 

btarunr

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i was gonna say because at the price of this thing you could probably go dual proccesor and quad SLI

At its price you can build an entire gaming rig that can run games faster than it, if it had a display output.
 
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At its price you can build an entire gaming rig that can run games faster than it, if it had a display output.

that's what i mean, a dual lga1366 build with dual 590
 
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I've gotten the impression that workstations cards have gotten better at playing games compared to their consumer counterparts, at least with the right drivers. However the last time I was able to see that put to the test was back with the G80s. Nobody bothers to test these things like that.
 
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I've gotten the impression that workstations cards have gotten better at playing games compared to their consumer counterparts, at least with the right drivers. However the last time I was able to see that put to the test was back with the G80s. Nobody bothers to test these things like that.

In most cases, the workstation card drivers are optimized for OpenGL, and specialty products, usually not DirectX. It's the reason they cost so darn much because the user base is so much smaller and the economy of scale in writing drivers just isn't there. From what I've seen the ATI and NV offerings are slight one offs from the consumer cards (in some cases, only very slightly). So, depending on the game 3D display means, the workstation cards will be about the same, or better in games.
 

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At its price you can build an entire gaming rig that can run games faster than it, if it had a display output.

that's what i mean, a dual lga1366 build with dual 590

you wouldn't even need that much, a 2600K and two GTX570's or 6950's would cream a single Tesla M2090 for cheaper.
 
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Are these GTX 580's with a different BIOS and drivers?
 

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Are these GTX 580's with a different BIOS and drivers?

no, they are completely different. like apples and oranges really.
 
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So basically they're saying that they achieved better performance by adding a gpu?

I could have told them that.
 

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These are not graphics cards ... at all.
The suite of AMBER program calculates molecular force fields and molecular trajectories of interacting molecules within a given fixed field.
The amount of data calculation to see what happens in a fixed multi-molecular field in 1 nanosecond is astronomical. The computations for single molecule interaction in staggering.
Doing 69ns in a 24 hour period is a real achievement, and I applaude Nvidia on their continued advancement into supercomputing.

Don't expect to see this in Duke Nukem Forever.
 
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Is this the new Folding@Home King? Either way, thumbs up, NVIDIA! :cool:
 

bear jesus

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Always good to see advancing computer tech help advance science.

But as this has four Tesla M2090 GPU's coupled with four CPU's and pumped out 69 nanoseconds of simulation in 24 hours, assuming perfect scaling how many would be needed to simulate in real time? :laugh: it's 6am so I'm not doing the math.
 
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Don't expect to see this in Duke Nukem Forever.
Why not it can be tessellation 10.0 :)
 
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Always good to see advancing computer tech help advance science.

But as this has four Tesla M2090 GPU's coupled with four CPU's and pumped out 69 nanoseconds of simulation in 24 hours, assuming perfect scaling how many would be needed to simulate in real time? :laugh: it's 6am so I'm not doing the math.

1 252 173 913 043.5 according to my PC calculator:eek::eek::eek:
(that's 1.25 trillion systems like the one in question)
 

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1 252 173 913 043.5 according to my PC calculator:eek::eek::eek:

:eek: OK we are probably quite a few generations away, probably after we hit the wall with current chip tech.
 
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That came with a 6GB on board. Ten times more expensive than a Gtx 580.
My logical GPGPU solution would be get the gtx 590 and 12GB kit. You can share system's memory anyway.
 
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I dont know if we will ever be able to compute real time life. With the limitations of size the only possible way would be quantum processing.
 
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