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April 12, 2012
In what will be a three-decade span between gigascale and exascale computing, HPC capability will have increased by a factor of one billion, but the apps that are projected to use this enormous increase in capability look pretty much like the gigascale ones. Are we missing opportunities as we push the apex of HPC higher?
Gigascale to Terascale
In February of 1991, the Office of Science and Technology Policy released the first “Blue Book” supplement to the President’s FY 1992 Budget Request for the new High Performance Computing and Communications Program. It was entitled “Grand Challenges: High Performance Computing and Communications” and contained a listing of the computational science and engineering challenges then seen as drivers for federal expenditures on HPC. Figure 2 from that report is reproduced below.
Petascale to Exascale
In preparation for the current attempt to secure federal funding for exascale computing, the Department of Energy conducted a series of workshops entitled “Scientific Grand Challenges Workshop Series”. While this series only focused on science and engineering areas of importance to DOE’s mission, that mission is broad enough to view the grand challenges discussed there as typical of the applications areas foreseen as drivers for the move to exascale.
With the use of a bit of poetic license to prevent the reader’s eyes from glazing over, the table below attempts to convey the general character to these early 1990s gigascale to terascale applications and the exascale applications considered for the 2018-2025 timeframe (depending on whose guess about the arrival of exascale computing one chooses).
We see that over a span of 28 to 35 years, depending on how you count, the applications list remains substantially the same. A few of the 90s applications have dropped off the list – either through success or loss of interest. A couple of well-established applications: Nuclear Physics and Nuclear Energy Systems have been added in response to renewed interest in nuclear energy. To be sure, the other areas listed – the ones surviving multiple decades – have grown in complexity and broadened in applicability. What seems to be missing is the addition of any fundamentally new applications.
Over the decades since the publication of that first Blue Book, “apexscale” HPC has grown in capability by a factor of 1,000,000. In another decade, when exascale machines occupy the apex, they will be a factor of 1,000,000,000 more capable than those early 90s machines. Certainly, this enormous increase must present the opportunity to do a few fundamentally new things.
Capability Computing Usage Modes
In general, as HPC grows in capability, it can be used in three distinct ways:
Clearly and justifiably, we are using apexscale HPC in the first two ways. But what about the third? Have we run out of new ideas? Certainly not. But getting new apps on the agenda seems to have been either remarkably hard or of surprisingly little interest.
Whether any new application candidate is, from inception, “exascale ready” seems considerably less important than its potential scalability. We are, after all, living in an age of scalable computing. Observe that many of the gigascale apps of the early 90s have readily survived, and thrived on, the transition to petascale and (soon) exascale. Did we coincidentally choose the complete collection of applications with this sort of potential for scalability back then or could there be others lurking in the wings?
Thinking of what we hadn’t thought of is always difficult and fraught with peril (you don’t know what you don’t know). However, the commercial and open science worlds have provided us with a few possibilities.
Although several federally-funded applications areas have well-established needs for data crunching (e.g., high-energy physics, bioinformatics, and national security), the current opportunity in “Big Data” comes from the commercial world. Think: Social Data Analysis, Personal Analytics, Biobank, the Quantified Self, 23andMe, Healthrageous, Integrated Personal Omics, MyLifeBits. These are probably just the tip of the big data iceberg.
IBM has already launched Watson, with (beyond Jeopardy) foci on health care and financial services. Cray and Sandia National Laboratories have started a Supercomputing Institute for Learning and Knowledge Systems. NeuStar and the University of Illinois Urbana-Champaign have created a Big Data Research Facility. The federal government is also getting onboard with its recently announced Big Data Initiative. In fact, it’s interesting to note that the “Blue Book” accompanying the President’s FY 2013 budget request is strongly focused on big data and not the grand challenges of earlier blue books.
So, Big Data is probably a “no brainer” for the new applications category. Some of it may not be exascale yet, but there’s lots of room to grow.
Brain in a Box
This new application candidate has been advocated by Henry Markram at the Swiss Federal Institute of Technology in Lausanne (EFPL). Its official title is the Human Brain Project (HBP).
As described in a recent Nature article, it’s “an effort to build a supercomputer simulation that integrates everything known about the human brain, from the structures of ion channels in neural cell membranes up to mechanisms behind conscious decision-making.” Markram’s precursor Blue Brain Project at EFPL estimates that this is an exascale application (see figure below).
IBM is also a player in the activity, with its cognitive computing project called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE). This project claims that “By reproducing the structure and architecture of the brain—the way its elements receive sensory input, connect to each other, adapt these connections, and transmit motor output—the SyNAPSE project models computing systems that emulate the brain's computing efficiency, size and power usage without being programmed.”
Thus, some form of simulation of the complete human brain seems like a keeper for our new applications short list.
Under this heading, a couple of systems immediately come to mind: the global energy system and the global social system. Each seems worthy of a modeling effort.
In this vein, the European Commission has recently funded a “Big Science” pilot project, called FutureICT, “to understand and manage complex, global, socially interactive systems, with a focus on sustainability and resilience.” FutureICT intends to accomplish these goals “by developing new scientific approaches and combining these with the best established methods in areas like multi-scale computer modeling, social supercomputing, large-scale data mining and participatory platforms.” Sounds like there’s potential for an exascale application here.
To the best of our knowledge, there is no current effort to simulate the complete global energy system. However, given the critical nature of energy, from resource discovery and recovery, through transportation of energy materials, to production and distribution of energy, and disposition of by-products, it seems like having one or more full-scale, high- fidelity simulation tools on hand might be a good idea. Perhaps this will be part of the FutureICT project.
The Whole Planet
Thanks to a concerted international effort spanning a couple of decades, we now have some pretty good global climate models. This community effort has also set a shining example for “team science.”
Lately, the climate modeling community has begun using the term “Earth systems science,” as more phenomenology is added to the basic coupled ocean-atmosphere simulations. Laudable and valuable as these efforts may be, they still leave most of the planet out of the models. So, maybe we should model the whole planet.
The opportunity for such a whole planet model is made visible when one looks at the imagery of our Blue Marble. One immediately notices how thin the shell of the atmosphere is in comparison to the dimensions of our planet. The Earth’s volumetric mean radius is 6371 km. Current climate models reach about 30 km above the surface. The deepest point any ocean model needs to reach is about 12 km below the surface. So, our current modeling efforts are focused on a shell that is, at best, about 0.66 percent of the Earth’s radius. This shell represents about 1.96 percent of the Earth’s volume and 0.02 percent of its mass.
Note that the sort of whole planet model proposed here represents an extreme example of a multi-physics, multi-scale problem. The relevant temporal and spatial scales range from sub-millisecond molecular interactions to multi-millennia ice sheet models to million cubic kilometer modeling of the ionosphere.
The advantages of a fully integrated whole planet model are readily apparent and include applications for:
The availability of such models would also serve to advance fundamental scientific understanding of our planet and its dynamics. Furthermore, undertaking to build such models would provide researchers in all of the relevant disciplines with a clear context for thinking about their research activities and how they contribute to the overall planet modeling effort.
Since the earth system models already in development will require trans-petascale computing capabilities, it is clear that exascale capability will be a bare minimum requirement for whole planet models.
The idea of building the sort of top-down whole planet model suggested here has also occurred to others. See, for example, the agenda of the Geneva-based International Centre for Earth Simulation (ICES). Furthermore, no discussion of this topic would be complete without paying homage to the ground-breaking efforts of Japan’s Earth Simulator Center.
Thinking outside the box
Making the case for new applications is a game that anyone can play. Here we have attempted to make the point that there may be worthwhile candidates lurking out there, beyond the view of our current exascale effort and its list of drivers.
If you don’t like these examples, please feel free to critique and improve them. If you have additional applications candidates, please make them known. The more frank and constructive discussion we have on this topic, the better and richer the future of HPC will be.
About the author
Gary M. Johnson is the founder of Computational Science Solutions, LLC, whose mission is to develop, advocate, and implement solutions for the global computational science and engineering community.
Dr. Johnson specializes in management of high performance computing, applied mathematics, and computational science research activities; advocacy, development, and management of high performance computing centers; development of national science and technology policy; and creation of education and research programs in computational engineering and science.
He has worked in Academia, Industry and Government. He has held full professorships at Colorado State University and George Mason University, been a researcher at United Technologies Research Center, and worked for the Department of Defense, NASA, and the Department of Energy.
He is a graduate of the U.S. Air Force Academy; holds advanced degrees from Caltech and the von Karman Institute; and has a Ph.D. in applied sciences from the University of Brussels.
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