Appro's HyperPower Cluster pulls together Intel's Xeon 500 Nehalem EP chips and Nvidia's Tesla GPU into a single solution aimed at improving the performance of HPC environments while lowering the costs. Nvidia's efforts to bring its GPU technologies into the mainstream come as rival chip makers AMD and Intel also are working on ways to merge their own CPU and GPU technologies.
Appro is rolling out a high-performance computing cluster that
combines Intel's Nehalem server chip and Nvidia's Tesla graphics
Appro's HyperPower Cluster, announced May 18, is the latest move by
systems makers to pull together CPUs and GPUs to offer improved
computing performance at lower costs and with greater energy efficiency.
"There's always been a need in HPC to find creative ways to run code
faster, and there's always been an interest in specialized CPUs," John
Lee, vice president of advanced technology solutions for Appro, said in
However, that hasn't caught hold because of the difficulty in
scaling a specialized chip industry to meet the demand, Lee said. Appro
officials have been looking at the idea of graphics processing units
for several years, but it wasn't until now that it made sense, Lee said.
He gave much of the credit to Nvidia, which has been aggressive in pushing its GPUs into the mainstream computing space.
"Nvidia is taking such a leadership role," Lee said. "They're driving demand that way."
Others also are moving in that direction. Advanced Micro Devices,
which bought GPU making ATI for $5.4 billion in 2006, announced May 6
that it was merging its chips and graphics businesses
, bringing the ATI unit fully into the AMD fold.
During AMD's annual stockholders meeting a day later, President and
CEO Dirk Meyer said that combining the company's CPU and GPU businesses
was a key differentiator
for the company going forward.
"Only two companies in the world can develop and deliver in volume
leading-edge x86 processor solutions," Meyer said during his talk.
"Only two companies in the world can develop leading-edge graphics, and
only one company-and that is AMD-has the ability to do both."
In addition, Intel is working on offering integrated graphics in its
upcoming CPUs, and is working on its own GP-GPU chip, codenamed "Larrabee."
However, both AMD and Intel have a way to go before they catch up with Nvidia in the GPU space.
"[Nvidia's] GP-GPU is definitely ahead of AMD Fusion [initiative for
bringing together its CPU and GPU capabilities], and Intel's Larrabee
won't even come out for years," Lee said.
Appro's Lee said that for businesses willing to do the necessary
coding to make their workloads run on GPUs, their cost savings over
running CPU-only platforms could be significant.
A key difference between CPUs and GPUs is the number of cores on a
piece of silicon, he said. While x86 compute chips can hold up to four
cores-with promises of six, eight and 12 down the road-a GP-GPU
(general purpose GPU) can have 800 or more cores, Lee said.
For workloads to take advantage of such numbers, they need to be
able to be broken up into many pieces, and to have those pieces
distributed among the cores. So while the GPU may not run as fast as a
CPU, because they are so many more cores, workload can be accomplished
Businesses can see improvements in processing performance of 10 or
more over CPU-only environments, Lee said, which is important to
companies being asked to do some legwork up front.
"There has to be an ROI story to this," he said. "The return needs
to be worthwhile [to the business]. Ten times gets to that point. ...
That's the part that Nvidia is working hard on."
Appro's HyperPower Cluster can execute thousands of concurrent
throughput parallel processing threads for problems that need high
mathematical computational capabilities. The cluster includes Appro's
high-density servers paired with an equal number of Nvidia Tesla S1070
The cluster includes interconnect switches for node-to-node
communication, a master node and clustering software in a 42U rack
It supports up to 304 CPU cores and 18,240 GPU cores.
IT managers also can use the Nvidia CUDA toolkit, which enables
users to take advantage of the massively parallel architecture.
Customers also get a choice of configurations and open-source cluster