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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  • martixy - Thursday, November 1, 2012 - link

    Thank you for this article! It was absolutely awesome to read through it and a nice break from the usual consumer stuff.
    Faith in humanity restored... :)
  • bigboxes - Thursday, November 1, 2012 - link

    I want to see the Performance tab on Windows Task Manager! :o
  • Abi Dalzim - Thursday, November 1, 2012 - link

    We all know the answer is 42.
  • easp - Thursday, November 1, 2012 - link

    For all the people speculating or suggesting that they should have used AMD GPUs or Intel CPUs, I think you need to think more like engineers, and less like "cowboys."

    To get started, reread this:

    "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."

    Now, why on earth would ECC memory on a GPU (which, apparently, AMD wasn't offering) be important? The answer is simple: because a supercomputer that doesn't produce trustworthy results is worse than useless. Shaving some money off the power and cooling budget, or even a 50% boost to raw performance and/or price performance doesn't really matter if the results of calculations that take weeks or months to run can't be trusted.

    Since this machine gets much of its compute performance from GPU operations, it is essential that it use GPUs that support ECC memory to allow both detection and recovery from memory corruption.

    As to the CPUs, I'm not suggesting that Intel CPUs are significantly less computationally sound than AMDs, but Cray and ORNL already have extensive experience with AMDs CPUs and supporting hardware. Switching to Intel would almost certainly require additional validation work.

    And don't underestimate the effort that goes into validating or optimizing these systems. Street price on the raw components alone has to be tens of millions of dollars. You can bet there is a lot of time and effort spent making sure things work right before things make it to full-scale production use.

    I know a guy, PhD in Mathematics who used to work for Cray. These days, he's working for Boeing, where his full-time-job, as best as I can understand it, is to make sure that some CFD code they run from NASA is used properly so the results can be trusted. When he worked at Cray, his job was much more technical, he hand-optimized the assembly code for critical portions of application code from Cray's clients so it ran optimally on their vector CPU architecture. When doing computation at this scale things that are completely insignificant on individual consumer systems, or even enterprise servers, can be hugely important.
  • CeriseCogburn - Monday, November 5, 2012 - link


    I note, with 225,000 plus AMD cpu's , they get barely over 2 petaflops.

    Add just 18,000 plus nVidia video cards, and ACHIEVE 20+ PETAFLOPS.

    LOL - once again, amd sucks, and nVidia does not.
  • Azethoth - Friday, November 2, 2012 - link

    So you are sitting at home playing monopoly on your iMac?
  • 2kfire - Friday, November 2, 2012 - link

    Can someone ban this joker?
  • Daggarhawk - Friday, November 2, 2012 - link

    Anand I LOVE this post. Breath of fresh air to get to see some of the real world applications for all this awesome tech we love. The interviews with scientists are especially fascinating and eye opening. Love the use of video to hear the insights, affect and passion of the researchers and see them at work. Please more of this sort of thing!!
  • armandc001-tech lover - Saturday, November 3, 2012 - link

    dammm what an article....!
  • philosofa - Saturday, November 3, 2012 - link

    Thank you Anand!

    I've been noting till I'm blue in the face that GK-110 formed Nvidia's backup plan, should the GCN/Kepler power ratio not have worked out as much to AMD's disadvantage as it did (presumably 'Big Fermi' was a similar action plan being enacted).

    It's not something I've seen anyone else say explicitly, so it's (confirmation bias aside) just lovely to hear that's your take too :)

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