Good afternoon and welcome. Thank you for the introduction. I'm going to spend a little
time today speaking a little bit more about the future. And I think there have been some
dramatic events over the last year as we all march towards exascale. And one observation
just starting by looking at the top 500 list is that the petaflop is pretty rare. How many
folks have a petaflop machine deployed just by a raise of hands? One, two, hopefully it's
not more than ten. Three. Okay. Thank you. Three. I see three hands. It's pretty rare.
But it's not hard to get to. All you need is cash. And if you have cash, you can buy
the cores today. But it's pretty, pretty rare when you think of ten disclosed systems in
the world getting to a petaflop. So we want to change that a little bit. Now let's just
look at, you know, not all petaflops are created equal. And along the way you have to make
choices. We're making choices at SGI. If we just look at the petaflop over at NASA,
world's largest, seventh largest disclosed system running standard Intel or commodity,
but standard Intel Xeon processors with no acceleration, just GPUs. Standard Linux. Standard
Infiniban. And I think this concept of standard is very important to gain the scale and gain
the cost efficiency and to gain wide industry adoption for scale. And, oh, by the way, it's
about 84% peak performance efficient. So not all of the rare ten petaflops are created
equally. And we very much like to evangelize standard Linux, standard Infiniban.
With that said, I am pretty excited about what I see over the next 12 to 24 months.
And I just stepped back and just looking at the x86 core curve. Just x86. Not looking
at non-x86 accelerators. If you wind the clock back to 1971, we introduced, the industry
introduced the one core. And it took near 30 years to get to dual core, 30 years, right?
And then another year to get to four core, another four years to get to eight core. Today
here in 2011, we have a ten core system. But we stand at the precipice of the fastest x86
core growth in the history of computing. It's coming our way over the next, over the next
two years. You know, going from ten core to some x number of cores, our projection is
roughly 64 cores over the next two years. And then by 2018, continuing that growth rate.
So we set at the precipice, the dawn of the fastest core expansion, the fastest x86 core
growth in the history of computing. So I'd like to take those cores out for a ride and
see what we can do. If we just sort of map that out a little bit at core count and look
at a sequence of years 2010, 2013, 2018, you know, by 2013 on kind of our roadmap, we'll
be able to achieve an x86 petaflop in four cabinets and have a megawatt per petaflop
performance of about 0.25. And decreasing even further, right? So we're in this massive
expansion of x86 cores and massive reduction in megawatts per petaflop. That's going to
allow us to build some pretty, pretty fantastic machines here in the coming years. So when
I think of exascale computing, I think there has to be two goals. Two goals. One is delivering
the eflop. You know, a billion IOPS, 20 to 30 megawatts, 256 cabinets, but the eflop
is going to be extremely, extremely rare. How many folks will have $200 million U.S.
to buy an eflop system by 2018? Show of hands. No one bold enough. Zero hands were raised.
So clearly the goal cannot be an eflop because no one in this room can buy one. So the goal,
the dual goal has to be to democratize the pflop where everyone in this room can raise
their hand and say I have a petaflop or more of computing. A million IOPS, 20 to 30 KVA
per cabinet, and mass adoption. I think we can get there. And with that, and that's why
our announcement with Intel today is so important, because we're making a choice. We've seen
enough of the mic architecture to say that it's real, it's going to be delivered, and
we're going to build the first system on this platform to democratize the pflop. And I think
this is an extremely important milestone for exascale computing with two goals, to deliver
the eflop and mass adoption of the pflop. And it's on the standard, I can't wait for
the hot seat session, by the way. I don't know what you ask a CEO, but I can't wait
of the questions covered by way. But it's on the x86 programming model. When I started
my career, I started as a compiler writer. And the first compiler I had to write was
for a WayTech code generator. I don't have many folks remember the 4187 WayTech co-processor.
But I longed for the days of just integrating the 287 and 387 into integrating the co-math
process with the integer math process or to get to one processor. I think one of the
great benefits of the mic platform is it's familiar. I'm not going to stand in front
of the entire community of the world and ask every programmer to rewrite their program to
get to exascale. So I think that the x86 programming model is a winner to get to exascale. And
again, this dual goal of being able to democratize the pflop and the pflop and the pflop and
mass adoption of the pflop. So what would one of these systems look like? So 256 cabinets.
And if we just look at this little old red dot down here, one cabinet with mic by 2018
on our projections, one cabinet would be 3.9 pflops. You build up to the next unit. Take
this concept of the pod or the cube, which we actually first developed at SGI, and self-enclose
the cell of eight cabinets. It's about 15.6 pflops. Just a row then expands to about 62
pflops and the entire data center is one eflop on this new architecture, x86 based by 2018.
So we have an expression in the U.S. called the seven year itch. So over the next seven
years, we're looking at the eflop in three phases. We're already designing systems in
the 10 to 25 pflop range on the mic architecture. If you're interested, we'd be more than happy
to talk to you about that. By 2018, we think we'll 2x that on the architecture to 100 to
150 pflops. And then by 2018, be able to deliver the one eflop. And nothing would delight
me more to be back here in seven years. And if you just follow Moore's law, I think the
smallest system, the smallest system on the top 500 list today is about five teraflops.
If you just follow that flop curve, that x86 curve, and about 20 times that over the next
seven years, every system, every system on the top 500 list by 2018 will be a petaflop
or greater by 2018. And we'll predict one or two eflop systems. Thank you.
Thank you, Mr. Baranecia. The first questions are coming now from Mr. Matzoka.
You don't know where to start, do you?
Well, I know where to start. Firstly, the lowest ranked machine on top 500 today is
40 teraflops.
I said 50. I'm sorry.
Okay. So having known SGI for a long time, not the history of the company, but its strength
has always been very large scale shared memory. And when you start envisioning, it's very
easy to accumulate flops because all you have to do is to kind of line up the ALUs. But
what's one of the biggest impediments we foresee as we move to exascale is how do we deal with
scalability, especially in terms of the strong scaling factors seeping in. Right now we have
less than a million cores, but we have more than a billion. And the ability of the applications
to scale, even a minute factor of strong scalability in applications will greatly compromise
the effectiveness of the machines to be utilized at effective utilization ratios.
So I thought SGI's competence was there. It has very fast shared memory, global address
space, which helps alleviate some of that problem. So are you going to continue down
that path exascale? Are you still going to have globally addressed shared space?
Or are you just going to give it up and say we're just going to piece up all the ones
you'll all use together at an exascale?
Yes, a good question. We're going to have multiple architectures in the market. One
architecture being our shared memory architecture, Altix UV. And today on Westmere, we scale
to 16 terabytes of memory, 2048 cores, one single system image, standard Linux, and we
support Windows. Windows doesn't scale to those peaks. That's a Windows limitation, not
a hardware limitation. When Sandy Bridge comes to market, the Romley architecture, we will
4X the size of memory we support, up to 16 terabytes, and we'll 2X the number of cores
to 4,096. And we'll keep on that TikTok with Intel to continue to scale shared memory.
We believe in shared memory very much. There's data intensive problems that are wonderful
for a shared memory machine. And data ingest problems. If you're taking big data in to
pre-process or post-process. So we're going to stick with our shared memory architecture.
Heck, we did a press release last year saying in our first three quarters, we've shipped
over 500 systems of UV. It's been a great first year.
On the compute intensive side, as we look at the architecture advancing to get to those
large-scale core deployments, it's a different architecture. And we think that Intel
Mike is the platform, the leading platform to get to that eFlop. So we'll continue to
invest for those workloads that gravitate toward shared memory. And with a stronger foundation
in the company, we're able now to do the R&D to get to this larger-scale core count on,
if you will, a scale-out architecture.
A second short question from Happy.
Thank you, Mark, for the presentation. I would like us just to look at your democracy, use
of the word democracy here. And it looks like it's achievable. We can get an exact scale
with your mic. But my question here is that for it to be democratic, it should be able
to be used. And you're not taking this community into confidence that will help them get the
applications to be used in that system. But the other thing that you forgot about democracy
is that to afford, it's about money. You didn't tell us anything about how you are going to
be able to get us to afford these systems. Most of that, you've got competition of Mike
at the moment. How do you deal with that?
So two questions in there. And we use the word democratize very thoughtfully. The application
world is going to have to change and optimize their code for those large-scale core deployments.
We have a team set up internally. We have over 25 folks in our company who do nothing
but understand all the application domains. We're not an application company today. We're
a facilitator in the space. So as we look at everything from open source codes, from
Gaussian all the way through closed source codes, computational fluid dynamics like Fluent
and ANSYS, they're going to have to advance equally with the hardware. And we hope to
be a facilitator in that space. In terms of making it affordable, by 2018, I think a petaflot
machine will range between one and two million dollars.