[Beowulf] [tt] Nvidia unveils Tesla, moves into supercomputing
eugen at leitl.org
Thu Jun 21 01:44:19 PDT 2007
----- Forwarded message from Brian Atkins <brian at posthuman.com> -----
From: Brian Atkins <brian at posthuman.com>
Date: Wed, 20 Jun 2007 16:23:29 -0500
To: transhumantech <tt at postbiota.org>
Subject: [tt] Nvidia unveils Tesla, moves into supercomputing
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Santa Clara (CA) – Nvidia today announced Tesla, a third product line next to
the GeForce and Quadro graphics products. The company aims to use Tesla cards
and the massive floating point horsepower of its graphics processors to take
over a portion of the lucrative supercomputing market.
The core of each Tesla device is a GeForce 8-series GPU as well as the general
component layout of the high-end Quadro FX 5600 workstation graphics card with
1.5 GB of memory. The only noteworthy difference between the FX 5600 and a Tesla
card is the fact that the supercomputing-targeted devices lack the graphics
outputs on the backpanel, which we were told, allows Nvidia to increase the
clock speed on Tesla.
While the actual clock speed of the Tesla GeForce GPU is kept under wraps,
Nvidia said that one processor (used in the C870 add-in card) is good for a
performance of 518 GFlops, two processors (used in the deskside supercomputer
D870, which integrates two C870 cards) will bring 1 TFlops; the Tesla GPU server
with four processors will hit 2 TFlops.
In terms of pure number crunching horsepower, Nvidia told us that one GeForce
GPU can match the combined performance of 40 x86 processors. In addition to the
raw performance, Tesla also makes a case for power efficiency: The C870 is rated
at a maximum power consumption of 170 watts and the GPU server at 800 watts,
which may sound a lot at first look. However, 40 low-power x86 processors would
run at a typical 1600 watts. With a common power budget of about 25 kilowatts
per rackserver, a Tesla GPU server rack has a theoretical maximum performance of
more than 60 TFlops – which would put the floating point rating of such a device
among the 15 fastest supercomputers currently ranked on the Top 500
Similarities to ATI’s stream processor card, implications for developers
Readers, who have been following recent general purpose GPU announcements, will
remember that ATI has product in its portfolio that is very similar to the Tesla
C870 – the stream processor card (which is based on a R580 GPU and 1 GB of
memory). Both products follow the same concept to make the massively processing
capability provided by shader processors available to run arbitrary code instead
of graphics code.
Developers such as John Stone and James Philips, senior research programmers at
the Beckman Institute of Advanced Science and Technology at the University of
Illinois, have been looking at accelerators such as GPUs for some, but have been
limited mainly by bugs in shader drivers. Stone told us that much of his work
with GPUs in the past was focused “on finding driver bugs” and “writing his
applications around them” in order to make the technology usable for scientific
simulations. “There can be a lot of rounding errors and because of this very
fact, I wasn’t very excited about working with GPUs,” he said.
However, both AMD and Nvidia came up with a programming model to solve this
problem. On AMD’s side, it is called CTM (“close to metal”) and on Nvidia’s side
it is CUDA (“Compute Unified Device Architecture”). At this time, it appears to
come down to personal liking which model is preferred by a developer, as, for
example, there are some universities that are working with CTM (such as
Stanford’s Folding at Home project) and there are some that are working with CUDA.
Stone and Philips are focusing on the Nvidia model as they claim its C++-based
language model is easier to deal with than AMD’s CTM version, which uses a
low-level assembly language.
While CUDA works very much like a regular programming model and, according to
Stone, can deliver results very quickly, the big challenge in exploiting these
devices will be knowledge to write advanced parallelized code for these GPGPUs.
Stone believes that especially coders who have written code for (massively
parallel) supercomputers before will have an easy transition opportunity. Of
course, knowledge of the hardware, graphics processing and a good look at the
parallelizable parts of applications help to take advantage of the technology.
Shane Ryoo, a graduate research assistant at the University of Illinois at
Urbana-Champaign, said that CUDA will allow programmers with some experience in
developing threaded applications to get “really good results right off the bat.”
However, it will be the fine-tuning process, which will increase the value of
GPGPUs: Ryoo noted that expert knowledge that will allow developers to squeeze
the best possible performance out of GPUs, sometimes can accelerate application
code by a factor of 5x or greater.
Nvidia is well aware of this challenge and has begun assisting universities in
establishing classes and developing course material focusing on massively
parallel programming and CUDA in particular. Eventually, the company hopes, that
GPGPU programming will become a standard part in computer science course work
and help to educate a whole new generation of programmers. So far, Nvidia has
taught courses at the University of Illinois, The University of California, the
University of North Carolina and Purdue University. Nvidia said that several
universities are developing their own courses, including the University of
Virginia, the University of Pennsylvania, Oregon State University, the
University of Wisconsin. Caltech, MIT, Berkeley and Stanford have been offering
“legacy” GPGPU and GPU programming classes, according to Nvidia chief scientist
The payoff: Accelerated applications
If the capabilities of these GPGPUs are exploited, there can be a big payoff.
Stone, who is working on Nanoscale Molecular Dynamics (NAMD) as well as Visual
Molecular Dynamics (VMD), said that a virus simulation that took 110 CPU hours
on a SGI Altix Itanium 2 supercomputer at NCSA required only 27 GPU minutes on a
GeForce 8 graphics processor – which translates into a 240x speedup.
In an example that showcases an impact that can touch many lifes, Ryoo and his
team are working on an interactive, medical MRI application that substantially
increases the resolution of MRI scans thanks to the added processing power. As a
result, they expect to be able to deliver much finer images, which allow
physicians to detect tumors at an earlier state or differentiate between a blip
or an actual tumor.
In a demonstration showed during an Nvidia event, a representative from
Headwave, a company that provides geophysical data analysis, highlighted a 4D
application, which allows users to visualize gigabytes and apparently even
terabytes of data in a three-dimensional scale and even apply a time filter to
display changes to geological layers over time. The company claims that GPUs are
accelerating their application by about 2000% and are delivering an output of
about 2000 MB/s.
In fairness, we should mention that Tesla (or stream processor cards for that
matter) will not be able to replace supercomputers, which continue to provide a
memory bandwidth a few Tesla cards cannot match. Scientists such as Stone
believe that products such as Tesla will make their way into supercomputers to
create an overall more balanced environment. “Number crunching was the limiting
factor up until now. Now Infiniband will be a problem,” he said.
GPGPUs are likely to have a greater impact on deskside supercomputers in the
short term. While scientists today have to apply for expensive supercomputer
time and in most cases have to wait several days until their application can be
processed - if those requests are not turned down anyway – there is now an
opportunity to run many of those tests on a desk right in the lab. Conceivably,
GPGPUs will allow more scientists to run more and higher quality simulations in
Cost and impact on the consumer
Nvidia’s Tesla products will start at $1300 for the single GPU add-in card; the
2-GPU deskside unit will run for $7500 and the 4 GPU server, which soon will
also be offered in an 8 GPU version, will sell for $12,000. Leaving out of
consideration that, at least to our knowledge, Tesla is not yet available, these
apparently lofty price tags turn out to be bargains at a closer look.
The C870 not only undercuts the ATI stream processor card, which currently sells
for about $2000, but also Nvidia’s own workstation products. The C870, at $1300,
compares to a Quadro FX 5600 graphics card, which requires and investment in the
neighborhood of $3000 and up. Clearspeed’s CSX600 accelerator card, which
provides a performance of about 100 GFlops, is selling in volume for about $7500.
A representative of Evolved Machines told us that the company plans to be
offering a 12 TFlops Tesla server, which will cost somewhere between $60,000 and
$70,000, but will be fast enough to match the floating point performance of the
19th fastest supercomputer on the Top-500 list.
Stone told us that even if the GPUs per se may appear to be expensive for a
consumer point of view, they “are available for far less money than the next
best thing that is available today.”
So, what does that mean for the consumer? Clearly, there is only an indirect
benefit for most consumers that we may see in improved research results down the
road. However, as all technologies, these GPUs will get cheaper over time and
even today, a $1300 card would be in reach for enthusiasts, who often spend
substantially more than $5000 on their rig. The fact is that there is no magic
necessary to make these cards work on a PC - and CUDA even works with GeForce 8
graphics cards, which can be had for less than $250 in the case of 8600-series
models. The real question is: When will there be applications that take
advantage of this technology and will they provide enough incentive for
consumers to purchase a GeForce 8 card? Industry experts believe that it will be
up do developers to come up with new applications that will take advantage of
the capability of GPGPUs on the desktop.
Nvidia CEO Jen-Hsun Huang told TG Daily that Tesla will be strictly focused for
the enterprise market and will not be making its way to the consumer market. In
the end, it will be up to the GeForce product groups to leverage CUDA on desktop
computers, but at least for now, Nvidia has little motivation to push this
technology for the average consumer: “Perhaps in the future,” said Huang, “[this
technology] could do physics on the PC, but this would need a Windows API.”
Singularity Institute for Artificial Intelligence
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