Cray Unveils Its First GPU Supercomputer

By Michael Feldman

May 24, 2011

Cray has released the details of its GPU-equipped supercomputer: the XK6. The machine is a derivative of the XE6, an AMD Opteron-based machine that the company announced a year ago. Although Cray is calling this week’s announcement the XK6 launch, systems will not be available until the second half of the year.

Cray’s pitch for the XK6 is that it enables applications to be productive with GPUs at scale. According to Barry Bolding, vice president of Cray’s products division, they are unique in the GPU computing space because of their long-term commitment to heterogeneous computing and their track record for building productive petascale systems. In addition, he points out that Cray has a legacy of experience with vector-based supercomputing and their associated compilers, most recently with their X2 super, aka “Black Widow.”

Hardware-wise, though, the XK6 is not that different from its CPU-based brethren. The blade is basically a variant of the XE6, replacing four of the eight AMD Opteron sockets with NVIDIA Tesla GPU modules. Each four-node blade consists of two Gemini interconnect chips, four Opteron CPUs, and four NVIDIA Tesla 20-series GPUs. The Tesla in this case is the X2090, a compact form factor of the M2090 module that was introduced last week. Like the M2090, the X2090 sports a 665 gigaflop (double precision) GPU, 6 GB of GDDR5, and 178 GB/second of memory bandwidth. A XK6 cabinet can house up to 24 blades (96 nodes), which will deliver something in the neighborhood of 70 teraflops.

Each XK6 node in the blade pairs a single X2090 GPU with an AMD Interlagos CPU (Opteron 6200), along with 16 or 32 GB of 1600 MHz DDR3 memory. That’s a rather CPU-rich configuration for a GPU-based server, given that many commodity clusters use two, four, or even eight graphics devices per x86 processor. And in many cases those processors are not top-of-the-line Xeons or Opterons.

According to Bolding, their thinking here is that not all supercomputing workloads are able to take maximum advantage of the GPU’s capability, so they’ve opted for a fairly conservative processor mix. “We really envision some applications spending a considerable amount of time running on just the x86 portion of the system,” he told HPCwire. “So we really want to have a balance between scalar and accelerator.”

On the other hand, some customers are likely to have applications that are already highly tuned for GPUs, and in this case would want a system with a higher GPU:CPU ratio. Bolding says, for those users they are willing to build custom machines with a richer GPU configuration, but it would not be the standard XK6 and would entail more than just a tweak to those blades.

Besides the hardware, the XK6 will inherit software stack and programming environment from the XE6, including the Cray Linux Environment (CLE). Added on top will be GPU-specific libraries and tools like NVIDIA’s CUDA SDK for programming the Tesla components. GPU support will also be provided by some of the third-party software that Cray currently resells, like the PGI compilers. The PGI compiler suite has already been extended to generate code for GPUs, and is being integrated and tested with the XK6 . The CAPS enterprise HMPP product for GPUs is also available, but unlike PGI is not currently part of XK6 test suite, and is not being resold by Cray.

Cray also will be developing additional GPU compilers, runtime libraries, and tools, as well as bringing in third-party software, such as EM Photonics’ CULA library, to make the environment richer and more productive. The idea here is to bring GPU acceleration in line with its Adaptive Supercomputing approach. The ultimate goal is to be able to write source code that could automatically be transformed to run on either CPUs, GPUs or some mix of the two. The goal is not just to deliver performance, says Bolding, but to “get your codes to better performance faster.”

To propel that vision, Cray is developing its own OpenMP-based compiler that will be targeted for GPU acceleration. This is a higher-level programming model than CUDA, using special directives within the application source to generate GPU code, much like what is available from PGI today. Unlike that offering however, the OpenMP version from Cray is based on standardized OpenMP directives designed to address hardware accelerators. A pre-production version will be available to selected customers, says Bolding, and will support both Fortran and C.

The directives-based programming tools for GPUs is a key part of Cray’s strategy to turn the XK6 into a productive GPU machine, and an attempt to differentiate it from the current crop of GPU-equipped clusters. Bolding says they expect to get the majority of performance available from lower level programming environments like CUDA, but in a much more productive and portable environment. And even though Cray is developing an OpenMP compiler, their overarching goal is to provide a standard, high-level programming environment that is portable across accelerators. “We really believe that a good programming model has to be hardware independent,” said Bolding.

The whole idea of GPUs in supercomputers, of course, is to accelerate codes amenable to data parallelism. One application set Cray envisions for these supers is weather and climate modeling, an area the company has been particularly successful in. For weather prediction, code acceleration can be especially critical, given the requirement is to deliver accurate results in real time. And for this application, model accuracy is directly related to floating point horsepower.

For example, it is estimated that for a weather forecasting model with a horizontal grid granularity of 40 km, one would need 0.4 petaflops. But to refine that granularity to 10 km, one would need a 20 petaflop system. The floating point requirements are similar for providing greater levels of granularity in climate simulations, but without the need to deliver results in real-time. Although these codes scale well enough on CPUs, the prospect of buying a system with 50 times as many processors to deliver more accurate results is daunting from both an upfront cost basis and the ongoing expense of powering such a system.

Today, the most feasible way to accomplish this level of performance boost is with accelerators. Not that GPUs are particularly cheap. An X2090 is likely to be four to five times as expensive as a top-of-the-line Opteron. But since a 20-series Tesla delivers about 10 times the raw floating point performance at only about twice the power consumption, a GPU solution makes sense as long as the codes can extract those extra FLOPS.

Once you’ve made the initial outlay, though, the XK6 is relatively easy to upgrade. According to Bolding, the next-generation GPUs, such as the future Kepler parts, will be able to be inserted into existing machines with just a module swap on the blade. Once those next-gen GPUs are available, Cray estimates these supercomputers will be able to scale up to 50 peak petaflops.

Also, one doesn’t have to build a pure XK6 machine. The GPU blades can be mixed into existing XE6 configurations, which themselves can be constructed from the older XT6 systems via the insertion of the Gemini interconnect.

Cray’s first XK6 order represents such an upgrade. The Swiss National Supercomputing Centre (CSCS) will use their existing XE6m system as the foundation for a multi-cabinet Cray XK6. The system is to be used to support scientific research in weather forecasting, climatology, chemistry, physics, material sciences, geology, biology, genetics, experimental medicine, astronomy, mathematics and computer sciences.

For the Swiss or anyone else to experience all this GPU goodness, they will have to wait for at least a few months. While the availability of the X2090 coincides with that of the M2090 announced last week, AMD’s Opteron 6200 isn’t expected to hit the streets until Q3. Whether Cray has other dependencies associated with availability, Bolding wouldn’t say.

Cray, of course, could have constructed a GPU machine last year based on the Magny-Cours CPU (Opteron 6100) and X2070 GPU.  HP, IBM, SGI, and practically every other HPC cluster vendor came up with a GPU offering in 2010. But according to Bolding, the XK6 is a more thought-out approach and will be the more productive machine.

“We could have come out with something earlier that wasn’t a complete Cray product, in other words, just a bunch of hardware and a mish-mash of open source software, but we chose not to,” he said. ”Our best mapping of a complete HPC product to the market aligned with the current Cray XK6 timeframe.”

“We’re not first to the party here, but we’re hoping we’re the best dancer,” he added.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Cray Posts Best-Ever Quarter, Visibility Still Limited

February 10, 2017

On its Wednesday earnings call, Cray announced the largest revenue quarter in the company’s history and the second-highest revenue year. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Leading Solution Providers

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This