Physical Architecture

The physical architecture of Titan is just as interesting as the high level core and transistor counts. I mentioned earlier that Titan is built from 200 cabinets. Inside each cabinets are Cray XK7 boards, each of which has four AMD G34 sockets and four PCIe slots. These aren't standard desktop PCIe slots, but rather much smaller SXM slots. The K20s NVIDIA sells to Cray come on little SXM cards without frivolous features like display outputs. The SXM form factor is similar to the MXM form factor used in some notebooks.

There's no way around it. ORNL techs had to install 18,688 CPUs and GPUs over the past few weeks in order to get Titan up and running. Around 10 of the formerly-Jaguar cabinets had these new XK boards but were using Fermi GPUs. I got to witness one of the older boards get upgraded to K20. The process isn't all that different from what you'd see in a desktop: remove screws, remove old card, install new card, replace screws. The form factor and scale of installation are obviously very different, but the basic premise remains.

As with all computer components, there's no guarantee that every single chip and card is going to work. When you're dealing with over 18,000 computers as a part of a single entity, there are bound to be failures. All of the compute nodes go through testing, and faulty hardware swapped out, before the upgrade is technically complete.

OS & Software

Titan runs the Cray Linux Environment, which is based on SUSE 11. The OS has to be hardened and modified for operation on such a large scale. In order to prevent serialization caused by interrupts, Cray takes some of the cores and uses them to run all of the OS tasks so that applications running elsewhere aren't interrupted by the OS.

Jobs are batch scheduled on Titan using Moab and Torque.

AMD CPUs and NVIDIA GPUs

If you're curious about why Titan uses Opterons, the explanation is actually pretty simple. Titan is a large installation of Cray XK7 cabinets, so CPU support is actually defined by Cray. Back in 2005 when Jaguar made its debut, AMD's Opterons were superior to the Intel Xeon alternative. The evolution of Cray's XT/XK lines simply stemmed from that point, with Opteron being the supported CPU of choice.

The GPU decision was just as simple. NVIDIA has been focusing on non-gaming compute applications for its GPUs for years now. The decision to partner with NVIDIA on the Titan project was made around 3 years ago. At the time, AMD didn't have a competitive GPU compute roadmap. If you remember back to our first Fermi architecture article from back in 2009, I wrote the following:

"By adding support for ECC, enabling C++ and easier Visual Studio integration, NVIDIA believes that Fermi will open its Tesla business up to a group of clients that would previously not so much as speak to NVIDIA. ECC is the killer feature there."

At the time I didn't know it, but ORNL was one of those clients. With almost 19,000 GPUs, errors are bound to happen. Having ECC support was a must have for GPU enabled Jaguar and Titan compute nodes. The ORNL folks tell me that CUDA was also a big selling point for NVIDIA.

Finally, some of the new features specific to K20/GK110 (e.g. Hyper Q and GPU Direct) made Kepler the right point to go all-in with GPU compute.

Power Delivery & Cooling

Titan's cabinets require 480V input to reduce overall cable thickness compared to standard 208V cabling. Total power consumption for Titan should be around 9 megawatts under full load and around 7 megawatts during typical use. The building that Titan is housed in has over 25 megawatts of power delivered to it.

In the event of a power failure there's no cost effective way to keep the compute portion of Titan up and running (remember, 9 megawatts), but you still want IO and networking operational. Flywheel based UPSes kick in, in the event of a power interruption. They can power Titan's network and IO for long enough to give diesel generators time to come on line.

The cabinets themselves are air cooled, however the air itself is chilled using liquid cooling before entering the cabinet. ORNL has over 6600 tons of cooling capacity just to keep the recirculated air going into these cabinets cool.

Oak Ridge National Laboratory Applying for Time on Titan & Supercomputing Applications
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  • mdlam - Wednesday, October 31, 2012 - link

    Will it run Crysis?
  • tspacie - Wednesday, October 31, 2012 - link

    Did you get any information about the network (yarc-2 , gemini) ? Cray's claim to fame has been their network architecture which is supposed to be a key contributor to the actual performance of the supercomputer.
  • thebluephoenix - Wednesday, October 31, 2012 - link

    They should have used Radeons 7970. You can buy 6 for the price of one K20, no ECC though (and for that is Fire Pro S).
  • HighTech4US - Wednesday, October 31, 2012 - link

    Toy GPUs have no place in HPC Computers.
  • thebluephoenix - Wednesday, October 31, 2012 - link

    1TFLOPS Double precision Toy?

    http://i.top500.org/system/177430
    http://i.top500.org/system/177154
  • garadante - Wednesday, October 31, 2012 - link

    You missed the point in the article saying ECC memory was a -must- for a usage scenario like this. With nearly 20,000 GPUs, and all of that information being continuously communicated between the GPU memory and the GPU itself, without ECC, errors would pop up very quickly, and would make useful computation nigh impossible.
  • HighTech4US - Thursday, November 1, 2012 - link

    Can you guaranty that the Toy GPU you recommend would not produce a single error on a software run that takes 6 months?

    You may accept an occasional graphics glitch while gaming but no HPC customer will.
  • RussianSensation - Wednesday, October 31, 2012 - link

    It's also about the specific software that works better with CUDA. GCN GPUs are no toys but the software support is nowhere near as prevalent in the professional GPGPU space compared to what NV has accomplished. This makes a lot of sense since NV essentially invented the GPGPU space starting with G80 in 2006. They spent a lot more money creating the CUDA eco-system and making sure they were the pioneers in this space. Given the higher widespread adoption of CUDA and proven track record of working with NV, larger companies are far more likely to go with Nvidia.

    This is actually no different than what we saw in the Distributed Computing space. For more than half a decade, NV's GPUs were faster in many apps. As the DC community is more dynamic and adopts much quicker to moder code and technologies, in the last 3 years, almost all of the new DC projects are dominated by AMD GPUs.

    On paper, HD7970 GE delivers 1.075 TFlops of DP and an 1200mhz 7970 has 1.23 Tflops. Without software support, for now it doesn't mean much in the professional space but the horsepower is already there.
  • mikato - Wednesday, October 31, 2012 - link

    Is this the supercomputer that will also be crunching away on the massive amount of data NSA is storing on everyone from strategic points in the telecom backbone?
    http://www.wired.com/threatlevel/2012/03/ff_nsadat...
  • Luscious - Wednesday, October 31, 2012 - link

    I'm curious if they ever went near F@H during burn-in and testing to see how much PPD that supercomputer could do.

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