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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  • piroroadkill - Wednesday, October 31, 2012 - link

    That sounds like a downgrade, no matter how you slice it..
  • extide - Wednesday, October 31, 2012 - link

    x2 I was thinking the same, especially at only 2.2Ghz!! I bet they are ~flat on CPU power and all the gain is from the GPU's.
  • SunLord - Friday, November 2, 2012 - link

    HPC is all highly multi-threaded by it's very nature which just happens to be about the only thing bulldozer is some what good at
  • Jorange - Wednesday, October 31, 2012 - link

    I wonder how many Petaflops this beast would have achieved if it used Sandy Bridge EP class chips? Anandtech's review of the Opteron 6276 vs Sandy Bridge Xeon EP showed that Intel was far more performant.
  • SunLord - Friday, November 2, 2012 - link

    I doubt will make enough of difference to be worth it given the main focus is all on the cuda gpu compute side
  • CeriseCogburn - Saturday, November 10, 2012 - link

    The AMD crap cores probably cause huge bottlenecks and lag the entire system and wind up as a large loss overall as they waste computer time.
  • Jorange - Wednesday, October 31, 2012 - link

    In a world in which millions of morons are enthralled by Honey Boo Boo and her band of genetic regressionists, it is great that scientists are advancing our understanding of the Universe. Without those 1%, one can only imagine the state our planet would be in.
  • IanCutress - Wednesday, October 31, 2012 - link

    I ported some Brownian motion code from CPU to GPU for my thesis and got a considerable increase (4000x over previously published data). Best thing was that the code scaled with GPUs. Having access to 20k GPUs with 2688 CUDA cores would just be gravy. Especially when simulating 10^12 and beyond independent particles.
  • maximumGPU - Wednesday, October 31, 2012 - link

    4000x ?! i don't think i've ever seen such a speedup, was that simply from 1 cpu to a 1 gpu?
    i ported a monte carlo risk simulation (which also uses brownian motion, although i suspect for different purposes than yours) and saw about 300-400X speed up, thought that was at the top end of what you can get in terms of speed increases.
  • IanCutress - Thursday, November 1, 2012 - link

    It helped that the previously published data was a few generations back, so I had some Moore's Law advantage. The type of simulation for that research was essentially dropped there and then because it was so slow, and no-one had ever bothered to do it on newer hardware. I think a 2.2 GHz Nehalem single core simulation of my code compared to a GTX480 version of the code was 350x jump or so. Make that 16 cores vs 1 GPU (for a DP system) and it makes it more like 23x.

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