SC13 Talk Pushes HPC in New Educational Directions
According to Dr. Thom Dunning from University of Illinois at Urbana-Champaign, science and engineering research has been revolutionized by computation but, to date computing has largely been used to organize, prepare for and disseminate courses. Dunning says that “The potential of using these technologies to teach students the fundamental principles of a subject through authentic computational simulation is largely unexplored.”
During a presentation today (Tuesday) as part of the SC13 Invited Speakers program, Dunning will discuss how “computational tools and simulations has been used to help teach high school and college chemistry. The simulations enable students to gain a deep, rich appreciation for the basic principles of chemistry. Further, the use of computational tools is enthusiastically embraced by teachers and students, results in improved performance of both, and leads to increased student interest in chemistry.”
Dunning’s talk is at 1:30 today at SC13 in Denver…a full interview is below that hits on the main topics he is set to discuss, plus a broader view of what tools and opportunities HPC pushes to other domains.
HPCwire: Can you give us a sense of what the various roles are for HPC in education—where does it fit in with scientific and broader educational goals?
Dunning: The overarching thesis I have is, as computational simulation has revolutionized scientific and engineering research, it can also revolutionize scientific and engineering education.
It’s very clear that computing and particular simulations had a major impact on most fields of science. It adds a new way for us to understand how the world operates, whether it’s the chemical world, the physical world, the biological world or some combination of all of them, which is of course what happens with global climate simulation. And we really have to think about how it we effectively use those kinds of simulations in education.
I’m a chemist by background, and all chemical phenomena are determined by molecules. But students never see molecules. It might at times almost sound to them like a fiction. But in fact they’re very real, and what you need to do for the student is to make them real. And that’s something we can do with computational simulation.
And in that case it could be as simple as allowing them to draw a molecule on a computer screen and manipulate it—turn it around, look at various sides of it, zoom in and out. Or it could be as sophisticated as taking a molecule and computing its vibrational spectra and then tying that in with global warming.
A teacher might ask why it is that the CO2 molecule and the buildup of CO2 in the atmosphere leads to global warming. To help students answer the question, the teacher could use computational simulation to show the students how the structure and the way the molecule moves is tied to the global warming phenomenon.
We have a project that has now been completed at NCSA for six years, called the Institute for Chemical Literacy through Computational Science. Although the name says “computational science,” computational simulation was really key. There we were looking at tools that would allow the teachers to work with students to visualize these kinds of molecular phenomena, and we saw a significant increase in not only teacher knowledge, but we also saw a significant increase in student knowledge as a result of this intervention.
HPCwire: So it sounds as though making science real and tangible then is one of the core components of what you’ll be talking about.
Dunning: Yes, and if you think about it, this is one of the issues that we have in science: many of the fundamental, underlying concepts from continental drift to the inner workings of the universe aren’t easily visualized.
But by having these simulations one can really make all of this very much more real for students and I think that’s the major benefit of computational simulation in the classroom. It really makes whatever the student is studying more real because they can interact with it and watch how it responds and thereby learn the behavior they’re supposed to be studying.
HPCwire: You’re a chemist by training, so is this the way you found your way into the high performance computing space?
Dunning: Yes, it certainly was a factor when I was an undergraduate. I went to a university that, when I was a sophomore, had just gotten a new computer: an IBM 1620. I was taking a course in quantum mechanics and suddenly it dawned on me that quantum mechanics was actually the basis for all of the chemistry that I had been taking. Then I started looking at what the equations looked like and I quickly realized I couldn’t solve them, and it took me a little while longer to realize that nobody could solve some of them. But with computers you could.
So that really got me interested in the use of computing to study various kinds of chemical research problems. And even for me it made it a lot more real—I was actually doing something. And I was manipulating chemistry at its most fundamental level: the molecular level.
When you go into the laboratory and you pour two solutions together, the outcome is being determined at that fundamental level, but that’s not what you see and that’s not what you experience. But when you actually do the computational simulations you can get a direct experience.
HPCwire: Speaking of that new generation, you’ve seen a number of trends come and go when it comes to computer scientists and students interested in HPC. Have you seen a dip or rise or stagnation around high performance computing as a career path? What ways are people coming into it? What trends have you seen over time?
Dunning: Well I guess what we’re seeing really is an increasing number of scientific communities that really need HPC. Either the models they use to describe the phenomena they’re interested in have gotten sufficiently complicated and sophisticated that now they need HPC to solve them, or on the data side communities now get to the point where the amount of data that they’re generating also requires HPC to be able to manage the data and to analyze it. So I think this is an area that is going to continue to grow over the next decade. And of course in chemistry it’s grown tremendously since I’ve been in the field.
I like to say that when I first started in the 1960s, if you had a conference that had a couple hundred theoretical and computational chemists in it, that was the world’s supply. Now there are thousands of theoretical and computational chemists—even tens of thousands. But even more important than that, even experimental chemists now routinely use computational simulation to gain insights into the experiments that they do. So it’s really expanded far beyond the folks who have traditionally done that type of work, to the point that I would say half of all chemists use computational simulations in one way or another.
HPCwire: So a lot of the interest in HPC then is being fed by the actual end-users of some of these applications? What about on the other side of it—people who are actively pursuing a career in high performance systems to help empower these systems. Are there some trends that you’ve noticed there?
Dunning: I don’t think I can be definitive there, but I can at least give you an impression. My impression is that when I started back in the 60s, most of the people that I ran into who were computer scientists were focused on scientific computing and what would today be called high performance computing, although it was CDC 6600s and things like that back then.
Now computing has gotten to be such a large, broad field that the number of computer scientists who are actually focused on high performance computing is now a pretty small fraction of the total field of computer science. And I would say that that area has certainly not seen the growth that computer science as a whole has seen because of exactly that.
HPCwire: Could you explain in more detail what some of those reasons may be?
Dunning: I think that for people who have an interest in computing, back in the 60s and 70s, scientific computing was the hot thing to do. In fact, it was the only thing you could do. Now if you’re interested in computing and you want to be a computer scientist, there’s a very broad field that you could be involved in: everything from the Web to real-time processing or embedded processing, and high performance computing is only a very small fraction of computer science at this point.
HPCwire: I see a lot of job postings and kind of “hipness” around the big data jobs and the label of “data scientist,” which is a way to call advanced computing something else completely. Maybe you just answered this question by what you just said, but do you want to comment on that transition into this culture of big data? There’s a piece of it in HPC, and a lot of these guys say, “Well, HPC has always been big data, it’s just data, and yes it’s gotten bigger.” There’s a little bit of push and pull between those two cultures, but there’s really nothing new.
Dunning: I think there’s a measure of truth to both of the comments. I think it’s correct for people in HPC to say, “We’ve been dealing with big data basically from the beginning. It’s been growing year by year.” And that’s true. On the other hand there are new sources of data now. There are sensor networks that are being put in place. All of the telescopes that are designed now basically are digital instruments—they are no longer analog instruments.
So there’s a huge explosion that’s occurring now both inside and outside of HPC. And I think that’s the big change that’s happened over the course of the past few years.
When I first came to NCSA in 2004 we were talking about the rapid increase in the growth of data. It took NCSA the first 19 years of its existence to archive a petabyte of data. By the twentieth year we had a second petabyte of data. And we’ve been adding substantial amounts of data into the archive ever since then.
But now we’re talking about instruments like the LSST, the Large Synoptic Survey Telescope. Once it’s in operation it will, by itself, generate something on the order of 100 to 200 petabytes of data. So in some ways we’re not quite there yet, but it’s coming at us pretty fast at this point. For these other sources of data are going to become as dominant if not more dominant than the amount of data that’s produced in high performance computing.
And I think that one of the things that’s going to happen here is that it’s not only going to revolutionize science and engineering, (the telescopes being a good example of how it revolutionizes science,) but it’s also going to revolutionize the behavioral, social and economic sciences also. Because as they get to the point that they start collecting the data, which is now so much easier to do because so much data is born digitally to begin with, they’re going to get new insights into the problems they’re studying that wouldn’t be possible to get otherwise.
HPCwire: It sounds like that brings us full circle back to the work that you’ll eventually be doing with the sustainable cities project, where you’re gathering sociological data plus sensor data plus GIS and other types of data, right?
Dunning: That’s correct. Looking at cities is putting all of that information together and really understanding what that data is telling you about how you can make the city operate more efficiently both energy-wise and people-wise.
HPCwire: Does this intersect at all with a project in the Middle East that Thomas Zachariah was doing regarding connected cities. Are these similar in nature?
Dunning: I’m not sure exactly what it is that Thomas is doing, but at NYU there’s a center called the Center for Urban Science and Policy and that’s one of its goal also: to use the city of New York as a testbed for understanding how you can improve the efficiency of cities and improve the quality of life in cities.
And a lot of this is drive by the fact that right now. I believe, in the U.S. about 50 percent of the population lives in cities and by the time we get to 2050 it’s going to be more like 70 percent. And this is a trend that’s happening worldwide, not just in the U.S So if you’re really going to focus on a major problem where you need to enhance energy efficiency and sustainability, cities are a natural testbed for that and one of the problems you really want to settle.
HPCwire: Is there any other takeaway from the talks you’re expected to give at SC that you want to share here and now?
Dunning: I guess my goal is to make this a call to arms—to really get the folks in HPC interested in talking to their colleagues in science and engineering.
Right now most of that is done in research, and I think it’s time for us to start that discussion on the teaching side. And I know here at Illinois I’ve had conversations with my colleagues on the faculty here and there is a lot of pent-up desire to introduce computational simulation into the undergraduate classroom because they know it could have a big impact. But it really takes a partnership between the folks in HPC and the faculty to be able to bring that about in each of the universities.
So I’m kind of hoping that people come away from my talk saying “You know, I need to go over and have a talk with Joe in Physics because I know he’s interested in computational simulation. I wonder what his thoughts are on using computational simulation in the classroom?”
I think it would just be terrific if you had a class in meteorology and the students each got to run their weather forecasts. In fact you change the initial conditions slightly so that all of a sudden when you say, “There’s a 30 percent chance of rain,” the students would really have a real understanding of what that meant because they could look at all the different simulations that all the other students ran to see in 30 percent of them there was rain in this area. It would make it really concrete as to what terms like that mean, which now people sort of having a feeling for, but not an in-depth understanding.
Same kind of thing with climate simulation. Having students run climate simulations and understand where some of the issues might be in some of the simulations and where the simulations are very stable. They’re all predicting very similar kinds of trends, which would give you much more confidence in that particular number. Then seeing where they aren’t producing things that are as consistent, so maybe that’s an area where the models need to be improved.
HPCwire: By the way, what are you up to now? We recently published news that you’ve retired as NCSA director…what next?
Dunning: Well, that doesn’t actually happen until December 31st, so I’m still officially the NCSA director. After that I will be joining Pacific Northwest National Laboratory and the University of Washington to help them set up a joint institute for advanced computing between the two institutions.
They’re really just formulating it at this point. This is an entirely new institute, so one of the things that we’ll be doing over the next couple of years is looking at areas of common interest between the institutions and looking for opportunities for them to collaborate on various projects in advanced computing.
And it will be both what I refer to as “scientific computing” and data analysis, which is the fundamental base for the activity, but it will also look at various kinds of applications. For example: exascale science and engineering, which involves looking at the science of cities to try to understand how they operate and how one can make them more sustainable and energy efficient.
But those specific areas have not yet been decided on—we’ll be holding workshops over the next couple of years to define what some of those major thrusts will be. And then there will be lots of individual collaborations between staff at Pacific Northwest National Laboratory and faculty at the University of Washington.
HPCwire: That doesn’t sound much like retirement.
Dunning: I really was planning on retiring. Although this is the second time I’ve tried to retire and as soon as I do people come knocking on my door, saying, “Hey, I’ve got something great for you to do.”
This one actually holds a special place for me in that when I was at Pacific Northwest National Laboratory one of my goals was, in fact, to get the two institutions, who are the two major research institutions in the state of Washington, working more closely together. So this is something that’s close to my heart.