Let me welcome Professor Dr. Thomas Stirling for his keynote presentations.
Thomas Stirling has such an outstanding list of titles that I only will pick up a few of them.
He is professor of computer science at Louisiana State University,
a faculty associate at the California Institute of Technology,
a CSRI fellow for Sandia National Laboratories, and a distinguished visiting scientist at Oak Ridge
National Laboratory.
He is probably best known as the father of Beowulf clusters and for his research on petaflop computing,
and not to forget he was awarded the Gordon Bell Prize with collaborators in 1997.
Thomas is our MBLIM speaker, and we are all happy that he will present to us his
HPC perspective under the title HPC Achievement and Impact 2010.
Thomas, please.
Thank you very much.
And thank you all. It's delightful to be back in Hamburg again, a little bit earlier this year, and
when I say it's delightful because I have been learning a lot about Icelandic volcanoes and
I don't know which would be worse, not being able to come here or not being able to leave.
So, so far we don't have the answer.
Every year I have the opportunity to
quickly walk with you through the achievements and high points of the last year, some of the issues, some of the successes, and
some of the disappointments. This is of course a personal view, a single person's perspective. I bring my own
biases, intending to be objective, but there are those things which interest me more than others. So this does not represent a perfect
objective perspective, but rather
an editorial or filter perspective.
Every year, and this is the seventh year, I tried to feel intuitively
what was the thing that I'm going to remember most about that year.
We struggled with this because as we know, as we move into the petaflops era,
we have several problems and challenges that in fact we're struggling with, and we've been discussing them at this conference.
Again, the successes, the failures, and the hopeful
accomplishments over the next year or so.
I'd like to convince you, however, that this year, 2010, may be remembered as the year that we ignited
exaflops, that even as we were learning to live with and benefit from petaflop scale systems, that we were beginning an
international process, a
large movement at
all different levels to move us into the exaflops era, to reverse a trend that our community
is familiar with and guilty of for the last few decades. In the past, the hardware has always
proceeded to software, and we've always had to try to play catch-up.
This time, this set of three orders of magnitude of advance,
our community, as a whole, leadership from many nations coming together, are
endeavoring to reverse that trend and to get the software in a position so that it's there when the hardware shows up, even more importantly,
that the software guides and informs the development of the hardware over the next seven to ten years.
So, briefly, I'm going to discuss some of the high points, many of which have already been touched on
over the last couple of days, and
showed how they juxtapose each other in the hardware, in the software, in the applications, and some of the people in their
accomplishments and accolades as well.
We're now moving into the period of hex core and beyond.
It was only a few years ago when dual core in the beginning of the multicore era
was playing, and each generation seems to move at almost a year as we move to the next step, not powers of two,
not quite the exponential rate we had anticipated,
but at least a steady increase in improvement. And you know, we don't really need to move much faster because, well,
the software hasn't caught up.
We see certain trends continue to move forward, such as the
pervasive nature of the clusters over the MPPs. This came as a surprise. We'll say more about this later.
We are embracing and engaging in the challenge of programming multicores, and even as we do so,
beginning to also
simultaneously undertake the challenge of heterogeneous computing, the use of GPGPUs in the context of multicores.
This is a kind of double whammy that is
challenging, and yet the community seems to be addressing it with a certain kind of austere
maturity that one does not always see as we move forward.
We see an interesting
confrontation taking place in the networking arena
between Ethernet and InfiniBand, and I'll say more about this in a few minutes, one in which there's this neck-and-neck race,
and it is too early to tell how it's going to turn out.
And
then there are some rather remarkable accomplishments in applications. True petaflop scale
applications. Sustained at
petaflops performance on real work, moving various science and technology domains forward.
We're doing a lot of talking about green computing.
I sometimes wonder about green computing. It seems like if someone didn't know about our field and they walked in,
they would think we were spray painting machines green,
because it's not clear how we're actually benefiting the world.
There's one machine that spends a tremendous amount of energy trying to figure out how to save a tremendous amount of energy, and
that might seem counterintuitive.
Let's of course start with the fastest machines. We've already discussed these,
but it's a good way to warm up, and it is of course my pleasure once again to recognize and acknowledge
the Jaguar Cray XT5 at Oak Ridge National Lab as
the number one machine, as we know, on the top 500 list. More importantly,
I'll show you, that this is not simply a machine with a number value,
but rather a machine that is turning out real work and that is becoming available to a wide community in order to advance a
number of different disciplines.
This I would call a real measure of success for petaflops
computing, and it's a kind of measure, the kind of effect that builds confidence in
those of us who want to use
ultra high-end computing as a tool in their own respective disciplines.
And I hope in the funding agencies internationally as they consider investing in future systems of this type.
But there's a surprise. I was careful about my metrics, wasn't I? I didn't say peak performance.
Well, I suppose you could put a pile of PCs in a very very large warehouse and count up the flops.
China has moved into second place with the Nebula machine.
Now, I had to rush to put this slide together for you, and I had a problem.
I went and I googled the China Nebula machine to get a picture of it, and I couldn't find any photographs on Google.
I guess Google had agreed to block them.
But this picture is appropriate. This isn't Nebula. If you know your astronomy,
that's the Crab Nebula, and it's appropriate because in fact, we know that this Nebula was created in
1054 A.D. because it was the Chinese astronomers that documented it.
There is a little neutron star in there spinning around a pulsar, and
with our best computers and our best telescopes like Hubble,
we're now able to actually see the time sequence of the explosion continuing there.
Seriously, this is a really dramatic accomplishment, a mix of multiple technologies.
It may not be an easy machine to program,
but it will be a machine on which a great deal will be learned, especially in China, and for which we hope
many important applications will be run, and its peak performance does exceed that of Jaguar.
Roadrunner is continuing also to still produce important work
using what was the first petaflop scale machine, and also
works with developing new techniques, new software tools for
the exploitation of heterogeneous processing and the use of very lightweight cores
optimized for certain kinds of data
idioms and
patterns of access.
For those of us who live outside of a lot of big fences and bobbed wire, Kraken is a particularly important machine.
In the top 10, it's a machine that's open to the broader community, including academics,
brings the same quality of capability that the machine like Jaguar and the other Cray machines do,
using the C star 2 router for the improved communication.
This is an important machine. One, I think, from an application side, will begin to see more and more
importance because of the diversity of access, too.
And
Jugene, a one-petaflops peak machine. I believe it's still the number one machine in Europe, and
this machine is also producing, as I will show you, a number of important applications in area and giving strong
experience within Germany on the running of a machine of this scale, and in particular the
development of the software and the software infrastructure, including the cyber infrastructure for
grid access to this machine.
Now, powering, and I use that term in more than one way,
powering these machines is the continued advancement of
microprocessor architecture and implementation over the last year, and significant, albeit predictable,
accomplishments were made, and I
would like to touch on these. We've already spoken about some of them, some of these. One
important is the step in number of cores to hex and beyond up to 12 AMD,
staying within the power factor of between 80 and 115 watts
per socket at about 2.3 gigahertz,
is giving us as many as 12 cores on their machine, plus a higher memory access bandwidth.
The Intel Westmere,
with some very impressive numbers, especially with Becton and the Nahel and the X,
6500, 7500 series, is providing an array of different
throughputs and integer and floating point that, with increased numbers of cores, that is
making it possible to have a number of different operating points within systems, different trade-offs for power versus performance, and for different
kinds of workloads.
Intel is responding to the diversity of the market,
needs with a diversity of
points of operation that are optimized for that, and this is a very important and very important strength. These newest Westmeers are at 32
nanometers, which is really pushing the the road map
aggressively and
gives great promise for the end of this decade.
If you haven't already been out into the room, you have to see that gigantic slab of wrought iron
at IBM, the, well, okay, wrought iron isn't this heavy.
This is a marvelous piece of engineering for the Blue Waters machine that will be installed, I believe, next year at
University of Illinois in Urbana-Champaign,
this enormous system based on the Power 7 architecture,
substantial performance
with
per core of over 30 gigaflops, and I'm reading the number here with a max of over a quarter of a teraflops per chip.
The piping, the plumbing on this, and the specific heat just to absorb the
the variances in power as it bursts through is it's a remarkable feat of
engineering.
NVIDIA continues its success
by moving from the Tesla accelerator to the Fermi GPU accelerator with
substantial performance,
single precision over teraflops, and
half of that for double precision and gigaflops, as you recall, at one time the earlier processing differences are a factor of eight
between a single precision and double precision and that gap has been dramatically reduced.
Equally, if not even more important is the continued advancement, I'll say more about this in a moment, of the software in
the software support, CUDA, which has received an enormous amount of attention.
You know, let me just speak to this. Every once in a while a trend or a product comes out that excites and
I can remember this, you know, almost two decades ago with clusters of PCs. There's something in the air about it.
Well, the NVIDIA family of machines that those accelerators have achieved the same thing. My students, students all around the country are
gravitate towards the opportunities of trying, even when difficult, to apply these machines and
it's helping to move our field forward, not simply in the amount of performance we achieve,
but in enticing and encouraging the next generation to join us.
We still face difficulties with the software and I'll come back to that in a moment. And of course AMD has the ATI Cypress
accelerator that also has very impressive numbers
with...
I'm not going to read through these. You can see the numbers themselves, but
very impressive when you get to over half a teraflops in double precision
with these chips as well. And the different paths that are being followed by these two in terms of their system
integration in some sense is a kind of shootout at the OK corral to figure out how in fact
ultimately we're going to converge to the steady-state systems, systems that will no doubt be heterogeneous
but we can be confident
we'll have structures that are more amenable to effective and efficient performance than sticking it at the wrong side of a PCI bus.
You know, I was here for the keynote.
I must have lost it, but where exactly was the slide on titanium?
Announcements this year
that both Red Hat and Microsoft have dropped their support for the Itanium processor. Now in fairness to
Intel, those were both very small market slices and had no real effect on that. HP still packages
Itaniums and delivers it to customers and the performances is still well.
But as we looked at the roadmap for supercomputing in the future
we should ask Intel, so what is the roadmap for Itanium? There is one, I've seen it.
But does the HPC community have enough confidence in that roadmap to invest its own time in system software and application software and algorithm?
That's a question mark.
Well, talk about hitting rock bottom.
During what some referred to euphemistically as a merger between Sun and Oracle, that is to say Oracle,
they dropped the very interesting
and certainly innovative rock architecture. Now in truth at that point in the development cycle the
the operational parameters were probably not the best and also
there were other experimental
architectures that were frankly doing better and it doesn't hurt to reduce your total portfolio of number of architectures.
But we lost rock. But now what do I say about the Intel Larrabee?
Well, I guess it's bounced back. It's the Phoenix. It has a new improved name.
So we'll have to see where that's going to go forward.
This is an interesting one.
This is where I wish I could put up both slides. So I'm going to do, we're going to do this time slicing thing.
Are you ready to get kind of nauseous? See there? Okay, now it's back there. Now it's back.
I don't know which one I'm going to end up keeping.
Right now we have a really interesting
knockdown drag out fight, a natural evolution between the two leading contenders for HPC networking.
Ethernet and in its various guises and in Finneband.
If we look at our top 500 list, it depends what your metric is.
There is a larger deployed base for Ethernet than there is, I would say, number of
individual machines than there is for
Infiniband. But the aggregate amount of performance dedicated,
exhibited by machines using Infiniband is primary private network versus Ethernet is
substantially greater. It's about a factor of two if we're looking at
sustained performance and it's at least a factor of 50% if we're looking at peak performance for obvious reasons.
The third player in this is really still part of the Ethernet family, but it is the evolution of 10 gig E.
Right now 10 gig is
too expensive to
to
have serious penetration. Somebody can correct me.
I didn't see a single system with 10 gig E on the top 500 list.
I don't mind if someone shouts out if I've got that wrong.
All right.
I think there was one back in November, but I'm not sure.
So how is 10 gig going to play in this? And is it possible that there's going to be some kind of a merger?
Actions are moving forward to see if there's some way to build one on top of the other or to make some kind of a hybrid
that would support the entire community needs and there everybody could benefit from economy of scale. This is a very interesting area.
I know that I want the
latency of
Infiniband. I would certainly like the price point of one gig E for, you know, 40 gigabits per second.
Actually, I'd like it for 80 gigabits per second.
You know, I could be even more ambitious than that.
We know that in order for the systems to go forward, we have to be able to make the networking go nuts.
So I'm not even going to try to predict how this is going to end up.
Well, I touched on the the top of the top, but it's worth mentioning some others.
I'm going to give this a shot. Tianhe.
Did I blow it?
Shanhe? Shenhe?
The second Chinese machine, also very impressive,
is
still in the,
I don't know where it is. Is it in the top 10? I think it is.
It is a machine that combines
the Intel parts with
others as well.
This is a machine that now has been shaken out and is being used effectively for both industrial and academic applications and is
apparently both stable and fairly effective producers. There are already some good applications that have come from this machine in China.
And I'm going to struggle again here. The Lamanosov.
Lamanosov? Lamanosov.
Suggestions?
This is very, very impressive because there's more behind this machine than just a single slide.
The Russians are rapidly building up a solid capability in system integration, deployment and management and maintenance. They're doing so across, well, the largest country in the world, but also in many other countries.
The Russia is taking on building incrementally a role of
major player in the management of industrial grade supercomputing.
And this machine, which is a third of a petaflops, is a very, very impressive system.
Europa or Geropa, the Germany's second machine, also an example of a canonical cluster. This, I believe, was integrated by Bull.
And Shaheen, a blue gene machine with tremendous aspirations. It's also about a fifth of a petaflops, but with the intentions of expanding it incrementally.
I read somewhere, they say, and once they get to a petaflops, then they're going on to exaflops. I'm sure that'll make Alan Garrow very happy.
Now,
you know, if we were to say a hardware machine is only good as its software, we might be in trouble.
Hardware machine is as good as its hardware, let's face it, and the programmers who program it. But there have been good improvements in software. In fact,
we're at a point where the industrial servers of the system software to our community is continuously to turn out more reliable, more efficient, better software.
I mentioned only a few here, you can see them on the slide. One of the things that I think is interesting is PGI's effort to integrate CUDA and OpenCL into
the other mainstream languages. Another organization that I'm watching is PathScale. You know, PathScale was some of the shrapnel that came out of the Sci Cortex debacle.
They have a very interesting business model that allows for very low overhead and yet a wide, diverse workforce. This is different than the standard
standard sweatshop methodology. It allows them to be very flexible and how they bring talent to their problems, provides a continuously distributed capability for local
visits at major, major sites that need support and allow them to adjust very quickly.
So Google releases new experimental programming language called Go.
And I was trying to picture the user interface. All I get is a large checkerboard with little white and black models that you move around this new tactile means of controlling the programming.
By next year, I'll know how to program in Go.
Other system software.
Right now, a big challenge is how we integrate our NPI-like codes or OpenMP-like codes with the accelerators such as the NVIDIA, Tesla and Fermi.
The use of CUDA, which was a giant step away from graphics languages that we had to struggle with. You know, let's see, I need to do a cross matrix multiply. Now, how do I use texture to do that?
So we've come a long way in our semantics of programming graphics processors for scientific computing.
But there's still a long way, a long way to go. And partly through applications experience and partly through a number of institutions endeavoring to do this kind of merger.
Real progress is being made. And I applaud NVIDIA in particular for their very strong outreach activities in bringing in academic cooperators, collaborators, just various kinds of institutions to provide a very rich and fervent dynamic environment.
What will come out of this, I will not predict. Truthfully, I find it difficult to have a five level of programming model as an effective way to move forward, let alone provide stability or reliability.
But this is something we're going to have to learn by experience. I don't think that we can we can just posit intuitively what the right answer is going to be.
So we're still waiting for open CL delivers reference implementations to be as effective as fine tuned as CUDA is and as much excitement as there is about it. You know, I've looked under the hood, the hood also and
You got to want to do it, you know, but there for just the right codes, the right algorithms, it can prove to be very exciting. So I'm expecting we're going to find some very interesting application next couple of years, maybe some Gordon Bell prize applications with open CL as as one of the the tools in the in the toolbox, helping to deliver the new levels of performance and efficiency both time and power efficiency.
Well, I like physics.
So there's something about graphene that really appeals to me.
You know, I'm a carbon based machine, and it just may be a natural affinity as I feel that I'm looking at a sibling, a sibling form emerging. Graphene has many very interesting properties IBM have demonstrated the laboratory transistors operating at over 100 gigahertz.
Nanotechnology Georgia Tech has been able to show a very fine lines of graphing 18 nanometers and they're moving, they're moving below that Georgia Tech has also done some very interesting physics modeling of the lowest levels of an issue to understand better the exact behavior for for real simulation of graphing devices that may in fact play an important role in ultimate nano nanoscale technologies.
Something is going to have to move us past the end of the of the silicon roadmap and.
And there was this cool thing about laying down patterns, you know, on the order of about an angstrom and that's it's a different problem. Well, I know you've all been waiting for the latest news on quantum computing.
The I have I've stated that as as an educator, as a professor at university, I consider trying to explain quantum computing to be like trying to teach computer science at Hogwarts. This is never helped me be able to, you know, explain how a thing can have multiple answers simultaneously.
There was some additional in the last year, additional algorithmic advances at MIT. They've shown how and now I have to read this.
Let me read this new algorithm developed at MIT shows how quantum computers might be used for solving linear equations. The trillion solutions to a trillion variable problem would be stored on only about 40 qubits.
Okay, that's the good news. The bad news is, however, you're going to extract all trillion solutions, you would do so serially and that would obviate the value of this. So as in so often quantum computing. It's really great unless you actually want the answer.
There have been other advances in the implementation, the claim in in nature of the first primitive processor is a quantum computer using two qubits. Figure that one out the time using on the first time using all electronic device.
I'm wondering what the the alternative with was. This is not my area of expertise. I picture some robot hand, you know, with a bunch of dice and throwing them and looking up what the answer is. But now it's all electronic other technology advances include advanced lithography using plasmas plasmas allow a shorter wavelength light on the order of these ultra the ultra violet area which can provide for quantum computing.
Finer finer grained line drawing and lithographic. That's very helpful. Another thing is an effort combining IBM and and Caltech researchers to use DNA to lay out the the bride scaffolding to lay out carbon nanotubes.
And I remember in 1994 at a meeting in Pasadena that a few of you in this room may have attended where Seymour Craig gave the keynote address and he was asked to talk about his view of the future and and he referred to it. He wanted to talk about what the really crazy fringe element fringe elements were and he talked about biological computing and he said he didn't think that
You know, biology itself would be very useful for computing. He couldn't think of a single example of an intelligent biological machine.
But he did feel that it would be a very useful manufacturing process for being able to arrange at the nanoscale devices and he may very well prove to be to be right. I'll be at two decades later.
And didn't we already see these
Or maybe I like them so much.
Okay.
Among the other other advances that are taking place are the cray XT six announcements and in particular what attracts me is and I'm about to move to it. Sorry. There is the new Gemini communication architecture.
With all of the advances in communicating networks. We still seem to be encountering this approximately microsecond latency barrier.
Between nodes. Once in a while we see something a little over that little better than that. But when when we end up from message to message. We're still over the microsecond
Advances in custom network architectures will help us to better understand how we may in fact move that network technology forward. So I'm looking forward to seeing these machines in place.
You know, deja vu. Okay, some applications.
In fact, this is a year in which multiple
Pedoflop scale applications have have emerged from the
Community doing real work. I just I cannot go through these individually, but they represent different problems on a number of the different high end machines, including
The machines at Oak Ridge machine that attack the machines that are gone a couple of machines in Europe.
And so forth. Some of these are really, really interesting, not just because they're pretty graphics, but the this this particular simulation with a full 3D simulation of the sunspot at a level of resolution and incorporating new phenomenology that have not been mastered before.
And you can see this is a simulation being done again in 3D. This other type
1A simulation, sorry, supernovas. This is where you have a dwarf star of equivalent that allows an accretion of material from a companion large star, usually a red giant to form in at a certain critical point goes explosive.
This is a particularly well calibrated phenomenon is able to be used for as a meter stick for predicting the distances throughout throughout the universe.
The longest, the longest measurement. It's the methodology that had been applied with use of supercomputers to determine the answer to the
The question, if the universe is slowing down, how fast is it slowing down and when will it turn around and start to collapse and the answer is, of course, none of the above.
The expansion of the universe we detect through the combination of astronomy and supercomputing is that the universe expansion is actually accelerating. It's growing faster.
Industrial applications this forum for Reynolds and it's it's f1 racers has also been used
And Roadrunner. Here's a here's an interesting application from Roadrunner doing some of the best resolution turbulent turbulence simulations.
The
I don't even remember this.
No, this
This was about galaxy formula formation and international team in many countries in the US and in Europe came together and using a number of different machines did some
unprecedented in depth simulations to show how galaxies nucleate and with a large black hole at their center is as early as 200 million years or so into the
into the lifetime of the universe, the earliest galaxies that we're able to pick up are somewhere around 800 million years from the beginning of the universe.
A lot of computer simulation is going to study various forms of diseases, the mechanisms and Parkinson's disease, which have been simulated in this application have direct application application to the same phenomenology for such critical diseases as Alzheimer's
disease and Huntington's disease. And so these calculations are truly now beginning to demonstrate new means of attacking these problems and unprecedented ways. Heart disease also incredibly important and these results.
Provided detailed simulations in the coronary arteries source of a tremendous amount of heart disease problems, heart attacks and stroke.
So,
There are trends. We have discussed the importance of cloud computing.
I'm of the opinion that cloud computing will be important to the HPC community and will be used for various kinds of
Throughput workloads and will also allow for rapid expansion of need under under certain pressures, but and I'm happy to be proven wrong. I think that these machines will be not optimized around latency issues which are shown to be critical to many applications applied in the supercomputing arena.
Nonetheless, there are many things that will learn from clusters. They will be, as we discussed earlier this week, they will be the first to use these very large amounts of power first to operate with as many cores and have to face the challenges of reliability. So cloud computing will impact HPC. It may not replace HPC, but it will be a forerunner that will inform the development of HPC systems.
I touched on a green computing before.
Exactly how we define the objective function for performance versus versus the carbon footprint is not is non trivial and it isn't obvious to me.
Yes, if you increase the performance, will you and you will in fact increase the carbon footprint? Well, not necessarily.
You will possibly even reduce the carbon footprint because it's the energy, not the power itself. It's the integral of the power and these different scaling properties, depending on the application will provide different results. So it's a it's a complex nonlinear objective function that has to be satisfied here and while I look forward to seeing the evolution of green computing.
I am not yet on board to understanding other than as an upper bound to how much computing we can bring to bear to applications. I'm not quite on board and understanding why this is the driver concern to the high performance computing community.
I mean, for example, in the green 500 list, I'm proud to say that the U.S. has more than 55% of the top top of the high performance computing community.
Green 500 list. Does that mean that we're better for the ecology because we're bigger on the list or does that mean we're just well doing a lot more computing and therefore hurting the ecology is more on the list, better or worse.
We are as a community and just look around us here. We are challenged by the graying of the HPC community and I say that referring to those of you who are fortunate enough to have hair to turn gray.
One of the things I congratulate Hans Maurer and his colleagues on is the aggressive effort that they have made in recent years to bring students in to this conference. This is very important.
There are many innovative and ambitious endeavors in the U.S., in Europe and in Asia that I know of related to finding new ways to excite youth and to educate them in the methods and practices of our field and more importantly, the importance of our field in many, many other fields.
I cite a couple of things and I beg your forgiveness but I'd like to just mention a couple of things that I and my colleagues at Louisiana State University are doing. We teach an HPC course and we do so over the Internet and we export it to other campuses, other states in the U.S. and even internationally.
It suffers from being just live real time and we will change that. It suffers because, well, it's different time zones and it's difficult to make sure that you can talk to California and the Czech Republic in the same class.
We also have every summer a week that we spend bringing high school students from around the state in Louisiana to the university purportedly to teach them about supercomputing and get their hands wet and get them to do it.
But what we're really doing is using this as an opportunity to teach them about themselves and while I cannot speak for the rest of the world, I know that in my part of the world, the biggest challenge we face is that there are no role models, there is no visible experiences that the youth of Louisiana and other parts of the U.S. can have access to to excite them, to motivate them to seek out the opportunities that are available.
And so these things are, these forms and these approaches are being undertaken. Also, University of Illinois or Banish Champaign, including the Great Lakes Consortium based around the Blue Waters Machine, is just yet another place that conducts multiple week seminars and tutorials and boot camps and schools to educate in using multi-core technologies and so forth. This is an important area.
We must pause for a moment to acknowledge the passing of one of our own. John Mucci died suddenly earlier this year. I knew John. John was one of those wonderful people who loved our field and he was one of those who embraced the excitement of advanced technology, the impact of that technology
on other fields and the entrepreneurial spirit that allowed people who wanted to make things happen to happen. I spent some real time with him after the fall of psychortex that he helped found and he was not demoralized, he was not depressed at all.
He saw this, even at his age, as a lesson to be learned and he was already asking people such as myself, what are those lessons? How do we go forward? What is the thing that should be done? And I'm really sorry to see that he did not have a chance to see through that next stage in his dreams.
Throughout this year, we have acknowledged many of our own and their accomplishments. Ken is here and was awarded, given the Cray Award for his many contributions to vector computing and a Fujitsu. The CPMD approach, Molecular Dynamics, the two contributors, Carr and Parinello were given the Fernbach Award.
Our friend and colleague, Bill Grop, received the IEEE TCSC medal for excellence in parallel computing and was also inducted into the National Academy of Engineering in the US for as many accomplishments, including, but not limited to, MPI, CH and PETC.
Fran Berman, formerly of SDSC, now I think she's at RPI, Jack just nodded yes, was given the Ken Kennedy Award for her many accomplishments and among other things, distributed computing.
And Blue Gene Supercomputer got the nod from Obama. That was kind of cool.
And I should send him a copy of my book on Beowulf. And important to those of us in the US, DARPA has a new director and we're taking a new direction and I don't know where that's going to lead, but many of us are excited about the prospects.
Gordon Bell prizes included those that sustained or exceeded the petaflops, those including those run on the Jaguar machine at Oak Ridge National Lab. It's very exciting to be at this new era in computing.
So as I prepared to conclude my remarks, I would like to bring to your attention, initiatives that have been undertaken over the last year.
A year ago, I reported to you a number of studies and workshops that have been held, studies conducted by DARPA, I participated in two of these, one in technology, the other in system software, a third was held on resiliency.
And then a large number of workshops that were conducted under the auspices of the department energy, primarily in the area of diverse applications domains, but also in the area of systems and algorithms.
Those original community held forums have now metamorphized into actual programs.
A number of people in this room have provided leadership in the first major international community efforts, the IESP or international exascale software project attempting to coordinate.
The efforts in all the continents, but I assume Antarctica, in the development of a combined and shared system software stack, the X stack, to allow us to share our needs, to share our talents and to share our resources so that we as a planet can develop one ensemble of tools and infrastructure where a field is simply not big enough to try to reinvent this over and over again.
And the clock is ticking the DARPA program after a substantial after over a couple of years of study has been established. This is a ubiquitous high performance computing program to literally reinvent supercomputing in preparation for the machines at the end of this decade, the.
The RFPs were distributed, the proposals have been accepted, the reviews have been made, it has not been yet announced, the winners are those announcements should occur by the end of this month, June.
This, if successful, will engage as much as nine years across four phases with multiple projects to develop new hardware and software techniques co-designed together with the with the discovery of new execution models to guide to guide those designs.
And I touched on that, and I think I would like to just introduce an annual event, and this is the canonical HPC system architecture. I want to show you, okay, I did this in the wrong way. So I and colleagues have gone through a major effort to analyze the top 500 list, and in fact, we've done it for over the many years to find the necessary centroids in the computer.
Sorry, centroids in the key parameters that have this is, by the way, motivated by our friend, Eric Stromire, who, who said, why don't you show them all to us and and why don't you explain how you do it? Well, I didn't want to do that and tell you, well, you know, throw like that.
So we took we took him in his word and through various analysis, and this is the place that we are today, we find that the the canonical performance is at about 27 teraflops for an average machine and comprising quad core Intel parts.
These being the Harper towns, which by by our means that dominate the field using Linux, of course, as the dominant operating system and still the ethernet
ethernet gig, gigi network, I've already talked to that, that problem as well. HP is the systems integrator for the canonical machine, it has now exceeded IBM in terms of deployed system and deployed aggregate performance and the architecture, of course, need I say, is the commodity cluster.
I had expected to see the to see a growth in the percentage of MPP machines, especially as they are growing and taking up a larger and larger part of the very top of the top 500 list, but the cluster is continue to penetrate into into this range. So in summary,
the last year has been one of steady accomplishment in systems in technology in processor architecture and perhaps most importantly in applications. It's also taken a giant step forward in projecting towards the future.
We prepare for the exascale hardware and software and the emerging new applications in science and moving towards applications and informatics and graph processing is beginning if anything to cause an acceleration in the effective application of high performance computing over the coming year. Thank you all very much.