Nvidia is preparing to roll out a new high-performance computing design that will allow OEMs to create what the graphics maker calls a "personal supercomputer." This HPC design from Nvidia will allow OEMs to build workstations that contain between two and four of Nvidia's general purpose Tesla GPUs and allow researchers to run scientific and other massive workloads at their desks. Lenovo, Asus and Dell are three of several OEMs that will use this design to build these Nvidia HPC workstations.
Nvidia is looking to bring the power of a supercomputer
cluster to the desktop.
the 2008 Supercomputing Conference in Austin, Texas,
demonstrate a new HPC (high-performance computer)
design that will allow OEMs to pack between two and four Nvidia GP-GPUs
(general purpose graphics processing units) with a workstation form factor.
Nvidia executives are scheduled to discuss the new HPC
design Nov. 18.
This new HPC design,
is calling the "Personal Supercomputer," is the latest effort by Nvidia
to bring its graphics technology into the supercomputing and high-performance
. While most HPC
clusters and supercomputers are powered by conventional CPUs, Nvidia is betting
that its GP-GPUs can offer the types of performance that scientists,
researchers and other workers in the HPC
market need now to run these types of massive workloads.
Unlike a traditional CPU, a GP-GPU contains hundreds of
smaller stream processing cores, which then allow an application's
instructional threads to run in parallel. Once the data is broken down into
smaller and smaller pieces, the GP-GPU allows for higher throughput and better
performance without relying on cranking up the clock speed to make the
application run faster.
So far, the market for GP-GPUs remains a niche part of the
overall HPC market. Nvidia has taken the
lead with its line of Tesla GP-GPUs, but Advanced
Micro Devices has also entered this market with its line of FireStream GPUs
In the next 18 months, Intel
will also enter the HPC market with a processor called "Larrabee"
product will be based on conventional processing cores.
In addition to its Tesla products, Nvidia has developed a
compiler and a set of development tools called Compute Unified Device
Architecture or CUDA, which allows application developers to use a variant of
the C programming language to program a GPU like a CPU.
Rob Enderle, an analyst with the Enderle Group, said that
while the market for GP-GPUs in high-performance computing remains a small part
of this space, he noted that Nvidia has made some significant strides in this
area and has shown that it attracts developers and now OEMs to its products.
"There are a lot of demands right now for supercomputers and
this type of design won't replace supercomputers as much as reduce the lines of
people waiting to use one," said Enderle. "Right now, Nvidia is ahead [of AMD
and Intel] but it's still an emerging market and we are still right at the
cutting edge in regard to where this is going."
While Nvidia and AMD
are focused mostly on selling these types of GP-GPUs to research institutions
and universities, Enderle said that both these companies are looking to expand
into other markets that include aerospace design and medical imaging.
The Nvidia HPC design is
based on the company's Tesla C1060 Computing Processor, which is made up of 240
stream processing cores. Each Tesla C1060 offers 4GB of dedicated memory and
933 gigaflops of single precision floating point performance. When OEMs use the
Nvidia design to create these workstations, they can create computers that use
two, three or four of these Tesla C1060 GP-GPUs.
Several PC vendors have lined up to offer new workstations
based on the Nvidia design, including Dell, Lenovo and Asus. Although Nvidia
did not provide a specific price, Andy Keane, the general manager of GPU
Computing at Nvidia, said these workstations would be priced at less than
$10,000. Some large research facilities, such as the University
of Illinois at Urbana-Champaign have
already begun experimenting with these types of individual workstations.
While the Nvidia supercomputer workstation design does offer
the convenience of working at a desk, Keane said these types of desktops would
not replace more traditional HPC clusters
and supercomputers any time soon. Nvidia's vision is to allow researchers and
others to move back and forth between an HPC
cluster and their desktops to reduce the amount of time it takes to work on
problems and crunch data.
"Right now, they have to do their work on the cluster," said
Keane. "A lot of researchers have regular notebook computers and then they
write the code on their notebook and then they have to go the cluster and
deploy the code on the cluster. What a lot of people now realize is that they
can get the efficiency and performance with a workstation that has multiple
GPUs. They are still going to have to use both, but a lot of the research can
be done on the desktop."