A Different Sort of Launch

Fermi will support DirectX 11 and NVIDIA believes it'll be faster than the Radeon HD 5870 in 3D games. With 3 billion transistors, it had better be. But that's the extent of what NVIDIA is willing to talk about with regards to Fermi as a gaming GPU. Sorry folks, today's launch is targeted entirely at Tesla.

A GeForce GTX 280 with 4GB of memory is the foundation for the Tesla C1060 cards

Tesla is NVIDIA's High Performance Computing (HPC) business. NVIDIA takes its consumer GPUs, equips them with a ton of memory, and sells them in personal or datacenter supercomputers called Tesla supercomputers or computing clusters. If you have an application that can run well on a GPU, the upside is tremendous.

Four of those C1060 cards in a 1U chassis make the Tesla S1070. PCIe connects the S1070 to the host server.

NVIDIA loves to cite examples of where algorithms ported to GPUs work so much better than CPUs. One such example is a seismic processing application that HESS found ran very well on NVIDIA GPUs. It migrated a cluster of 2000 servers to 32 Tesla S1070s, bringing total costs down from $8M to $400K, and total power from 1200kW down to 45kW.

HESS Seismic Processing Example Tesla CPU
Performance 1 1
# of Machines 32 Tesla S1070s 2000 x86 servers
Total Cost ~$400K ~$8M
Total Power 45kW 1200kW


Obviously this doesn't include the servers needed to drive the Teslas, but presumably that's not a significant cost. Either way the potential is there, it's just a matter of how many similar applications exist in the world.

According to NVIDIA, there are many more cases like this in the market. The table below shows what NVIDIA believes is the total available market in the next 18 months for these various HPC segments:

Processor Seismic Supercomputing Universities Defence Finance
GPU TAM $300M $200M $150M $250M $230M


These figures were calculated by looking at the algorithms used in each segment, the number of Hess-like Tesla installations that can be done, and the current budget for non-GPU based computing in those markets. If NVIDIA met its goals here, the Tesla business could be bigger than the GeForce one. There's just one problem:

As you'll soon see, many of the architectural features of Fermi are targeted specifically for Tesla markets. The same could be said about GT200, albeit to a lesser degree. Yet Tesla accounted for less than 1.3% of NVIDIA's total revenue last quarter.

Given these numbers it looks like NVIDIA is building GPUs for a world that doesn't exist. NVIDIA doesn't agree.

The Evolution of GPU Computing

When matched with the right algorithms and programming efforts, GPU computing can provide some real speedups. Much of Fermi's architecture is designed to improve performance in these HPC and other GPU compute applications.

Ever since G80, NVIDIA has been on this path to bring GPU computing to reality. I rarely get the opportunity to get a non-marketing answer out of NVIDIA, but in talking to Jonah Alben (VP of GPU Engineering) I had an unusually frank discussion.

From the outside, G80 looks to be a GPU architected for compute. Internally, NVIDIA viewed it as an opportunistic way to enable more general purpose computing on its GPUs. The transition to a unified shader architecture gave NVIDIA the chance to, relatively easily, turn G80 into more than just a GPU. NVIDIA viewed GPU computing as a future strength for the company, so G80 led a dual life. Awesome graphics chip by day, the foundation for CUDA by night.

Remember that G80 was hashed out back in 2002 - 2003. NVIDIA had some ideas of where it wanted to take GPU computing, but it wasn't until G80 hit that customers started providing feedback that ultimately shaped the way GT200 and Fermi turned out.

One key example was support for double precision floating point. The feature wasn't added until GT200 and even then, it was only added based on computing customer feedback from G80. Fermi kicks double precision performance up another notch as it now executes FP64 ops at half of its FP32 rate (more on this later).

While G80 and GT200 were still primarily graphics chips, NVIDIA views Fermi as a processor that makes compute just as serious as graphics. NVIDIA believes it's on a different course, at least for the short term, than AMD. And you'll see this in many of the architectural features of Fermi.

Index Architecting Fermi: More Than 2x GT200
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  • AlexWade - Wednesday, September 30, 2009 - link

    How long have you been working for NVidia?
  • taltamir - Thursday, October 1, 2009 - link

    don't insult nvidia by insinuating that this zealot is their employee
  • dzoni2k2 - Wednesday, September 30, 2009 - link

    What the heck is wrong with you SiliconDoc?

    Since when is memory bandwidth main indicator of performance?!

    For all I care Fermis memory bandwidth can be 999GB/s but what good is that if it's not used?
  • SiliconDoc - Friday, October 2, 2009 - link

    I'm sure "it won't be used" because for the very first time "nvidia will make sure it "won't be used" becuase "they designed it that way ! " LOL
    You people are absolutely PATHETIC.

    Now the greater Nvidia bandwith doesn't matter, because you don't care if it's 999, because... nvidia failed on design, and "it won't be used!"
    Honestly, if you people heard yourselves...
    I am really disappointed that the bias here is so much worse than even I had known, not to mention the utter lack of intellect so often displayed.
    What a shame.
  • PorscheRacer - Wednesday, September 30, 2009 - link

    Exactly! R600 had huge bandwidth but couldn't effectively use it; for the msot part. Is this huge bandwdth the GF300 has only able to be used in cGPU, or is it able to be used in games, too? We won't know till the card is actually reviewed a long while from now.
  • SiliconDoc - Wednesday, September 30, 2009 - link

    What a joke. The current GT200 responds in all flavors quite well to memory clock / hence bandwith increases.
    You know that, you have been around long enough.
    It's great seeing the reds scream it doesn't matter when ati loses a category. (no actually it isn't great, it's quite sickening)
  • SiliconDoc - Wednesday, September 30, 2009 - link

    Yes of course bandwith does not really matter when ati loses, got it red rooster. When nvidia is SO FAR AHEAD in it, it's better to say "it's not double"...LOL
    What is wrong with you ? Why don't you want to know when it's nvidia, when it's nvidia a direct comparison to ati's card is FORBIDDEN !
    That's what the author did !
    It was " a very adept DECEPTION" !
    Just pointing out how you get snowballed and haven't a clue.
    Rumors also speculated 4,000 data rate ddr5

    4000x384/8 - 192 bandwith, still planty more than 153 ati.

    CLEARLY though "not double 141" (nvidia's former number also conveniently NOT MEWNTIONED being so close to 153/5870 is EMBARRASSING) - is 282...
    So anand knows it's 240, not quite double 141, short of 282.
  • DigitalFreak - Wednesday, September 30, 2009 - link

    Looks like SnakeOil has another alias!
  • therealnickdanger - Wednesday, September 30, 2009 - link

    Agreed. That was refreshing!
  • mapesdhs - Wednesday, September 30, 2009 - link

    Blimey, I didn't know Ujesh could utter such things. :D When I knew
    him in 1998 he was much more offical/polite-sounding (he was Product
    Manager for the O2 workstation at SGI; I was using a loaner O2 from
    SGI to hunt for OS/app bugs - Ujesh was my main contact for feedback).

    The poster who talked about availability has a strong point. My brother
    has asked me to build him a new system next week. Looks like it'll be
    an Athlon II X4 620, 4GB RAM, 5850, better CPU cooler, with either an
    AM3 mbd and DDR3 RAM or AM2+ mbd and DDR2 RAM (not sure yet). By heck
    he's going to see one hell of a speed boost; his current system is a
    single-core Athlon64 2.64GHz, 2GB DDR400, X1950Pro AGP 8X. :D My own
    6000+ 8800GT will seem slow by comparison... :|


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