EDUCAUSE | Supercomputing

Making Research and Education Cyberinfrastructure Real

elilly — Thu, 26 Jun 2008 11:25:51 -0500

Providing an evolving foundation for 21st-century research and education, cyberinfrastructure is both a focus for invention and an accelerator of innovation, linked through a trajectory that begins with design and evolves to broad-based use.

IEEE Supercomputing 2007

khascall — Mon, 19 Nov 2007 13:08:17 -0600

This year was my fourth time attending the IEEE Supercomputing conference. My experiences at Super are almost opposite from my experiences at most other conferences I attend. At SC, I tend to hear information that I can't immediately apply back on campus (deeply scientific presentations based on highly advanced projects conducted on heavily specialized equipment). I'm surrounded by people whose occupations tend to be substantially different from mine (professors and scientists, grid/cluster administrators and scientific programmers). It sure isn't Educause.

So, what am I doing here? Other than sometimes bumping into my colleagues from the CIC Research Computing Group, I'm here as a trend-spotter and as an observer of the field and of faculty. I speak entirely in that capacity, and I'd like to share with you my highly un-scientific observations for your consideration.

Data

One trend I've been tracking actually started back at the first SC I attended, in Phoenix in 2003. Many of the presentations at SC 03 included an observation by the presenter that modern techniques (whether smashing particles together or embedding sensor networks into the US highway system) were producing a vast quantity of data, and further that this quantity of data presented a number of unique challenges which had no real solution at the moment.In subsequent years, discussions of the problem of data and metadata seemed to oscillate between anxious pessimism and shrugging optimism -- either a solution would be found, or it wouldn't. Disks got bigger, memory got faster, and data description standards evolved. This was business as usual. I was disappointed to observe that no one was referring to the historical guardians of data and information architecture: the Library. But that could be a blog post all on its own.

However, in SC06, the game-changing realization seemed to be that a new strategy toward scientific discovery was emerging -- or at least a historical strategy was re-emerging as relevant: the notion that one can make discoveries not only via the strict scientific method (hypothesis leading to an experiment to test that hypothesis leading to refinement of the original hypothesis) but also via data mining and mashing data together without necessarily having a hypothesis or an explicitly designed experiment in the picture. When you have scads of data, former outliers might well turn out to be the signs of some new discovery.

At SC07, I was curious to hear how attitudes toward the data deluge had evolved. The current answer seems to lie in visualization, building on the data-mining trend from last year. This only makes sense as a further step -- if you have so much data that you struggle to store it, surely you also have too much data to look at it number-by-number. In general, the strategy seems to be to visualize data and observe overall trends and behaviors in the visualization, and to convert that observation into further areas for hypothesis and experiment. In addition, there is a growing use of simulations to see behaviors which one cannot (or cannot YET) observe, and then to go looking for that behavior in the world.

The final session of the week (HPC Survivor!) was a humorous look at the storage dilemma, with "experts" describing and defending various future architectures -- expert maintenance (vast disk rooms tended by acolytes); continual expansion via high-capacity portable modules (just keep buying big shipping containers of disk, bury them underground, stick them on planes); the cloud approach, blasting data into cyberspace (the Google approach, zero cost -- hah -- and streamed back to you from some "other" place); and iPods (extreme miniaturization -- from the iPod nano to the iPod femto, 1TB of storage on an earring). One participant asked "Will exaflops of storage be necessary if computers are sufficiently powerful to re-compute the calculations?" I think he was kidding...but...

Hardware/Software

The overall concern for the environmental impact of powering supercomputers was much higher this year compared to last year. This year also marked the rise of the "Green 500" as a contrary measurement to the traditional "Top 500" as a measurement of the most powerful computers.

Another observation I made this year is that there seems to be less concern about hardware capabilities (unlike SC05 & SC06, which was all about the petabyte and the need for petascale computing to address various critical questions) and more about software -- code that will run in an optimal way, which can be written and verified quickly enough to make the computational scale relevant. After all, it's no use having the fastest computer in the world to run your code if you can't code the algorithms you need in a reasonable amount of time. Everything will have moved on before you finish. I take this to be a significant open question -- where will the software for these clusters come from ("the community" is a conveniently nebulous answer), and how will it be written (given the overall dimunition of instruction in Fortran).

The use of non-traditional boards (like the PS3 processor and graphics cards) for particular computations continues. Now that more and more of these boards have double-precision, their usability for scientific calculations has increased. The ability to code directly against the hardware gives you full control over how the processor gets used, but also starts to suggest the need for tools to abstract away the challenges of thinking about millions of cores. The relatively small amount of on-chip memory on cell processors in particular leads to a different way of thinking about problems -- tiny, super-optimized code done in discrete chunks. These new boards are leading to new techniques for developing parallel applications, but the standardization and spread of these techniques is only beginning.

The Bleeding, Bleeding Edge

One of the most fascinating trends I had an opportunity to hear about is a reflection of work by individuals such as MIT's Neil Gershenfeld and David E Shaw of DE Shaw Laboratories -- the transcendence of computers from a thing in a box and into the thing itself.

Shaw's example of this connectedness is a very tangible one -- his laboratory is developing a specialized chip, called Anton. Anton optimizes the solution of a particular group of problems in molecular dynamics (how proteins form and fold). In the case of Anton, the structure of the problem are echoed in the structure of the chip to solve the problem -- the steps of the solution process are in the hardware itself.

The inevitable mixing of concepts that results from this kind of connectedness was readily reflected in the questions that followed Shaw's talk -- when asked about energetics and quantum behavior, it was necessary to carefully distinguish whether the question was about the problem, the algorithms or the hardware. The atoms and molecules being studied are starting to converge with the structure of the thing which studies them. Fascinating.

Neil Gershenfeld's keynote included examples were a step or two further removed from immediate practical use, but gave a further insight into what it might mean for the future. What if programming "bits" -- those 1s and 0s -- and programming "atoms" -- fabricating new materials, components, and structures -- was one and the same? At a basic level, one can think of a computer program as a structured orchestration of logic gates -- pathways of ANDs and ORs, or at a higher level, IFs and NOTs. Progress in programming computers proceeded from directly wiring these gates to expressing the desired wiring paths by means of various input devices -- cards, text, drag and drop -- which converted the input back into the basic gates again. What if we used all we have learned to go back to the beginning?

One example he cited was a "paintable computer" -- if you express the logic gates of a problem in terms of dots on a wall, you might imagine a wallpaper which turns a wall into a supercomputer. If you needed more computational power, you might paint another layer on top. It brings a whole new meaning to redecorating your office! Another example he gave conjectured was a building automation system. Programmable materials would allow the building infrastructure to be composed of materials which constantly maintain a sense of the building's own use of energy, the air flows and temperatures, the status of doors and windows, and which adjusted itself dynamically over the course of a day to avoid waste and pinpoint problems.

Programming in atoms has fabulous and exciting implications for the future. When you smash the idea of the programmable atom with the emerging notion of the "fab lab" -- rapid prototyping systems like 3D printers which are evolving toward the capabilities of the Star Trek replicator -- the picture of a powerful creative force for humanity starts to emerge.

State of the Field

The overall theme of this year's conference was competitiveness and industry, which meant that a number of the invited speakers were representing industries such as automotives, insurance, and entertainment. One of the most interesting presentations I listened to was given by Michael Resch from Stuttgart's HLRS http://www.hlrs.de/, who described the public/private partnership between the university and nearby automotive and machinery companies. The arrangement was somewhat complex, but seemed to balance the needs of all involved. Essentially, the company is a shell for organizing the purchasing, operation, and reselling of cpu cycles. The center is university-owned, the computers are corporate and state purchases, and the company is co-directed by an industry representative and a university representative.

They re-sell cycles to small start-ups as well as the constituent companies. They grant high priority to industry jobs as needed, but at a premium price. The countering benefit to university researchers (who might find their jobs pre-empted by a last-minute automotive simulation) is access to 3x-5x more overall power than could be purchased without the partnership, access to a diverse array of architectures, and shared labor costs.

Although the industry presentations were interesting to listen to, they generally fell into a later part of the technology curve, rather than the bleeding edge of pure research. These accounts of the virtues of semi-commoditized HPC in service of industry was a nice balance to some of the extremes of the pure research presentations. Overall, however, the range of presentations left a rather large hole -- the use of HPC (or just massive but semi-commoditized computation) by researchers outside of physics and industry.

I definitely missed the presence of research presentations from fields like the humanities and social sciences. I hope that this absence does not reflect a trend of the industry as a whole. However, when I shared this observation informally, everyone I spoke with said that even within traditionally HPC-oriented disciplines, sub-fields and talented people are being left behind by the HPC community.

This year's SC was larger than past years (9000 this year), and more inclusive from a socio-demographic perspective than past years -- more women, more minorities -- but the inclusion of projects from disciplines outside of physics/astrophysics was lower than in years past. I think the key challenge for HPC continues to be the spread of HPC to more people in more fields.

Cyberinfrastructure Vision for 21st Century Discovery

drupal — Fri, 06 Apr 2007 13:56:44 -0500

"NSF's Cyberinfrastructure Vision for 21st Century Discovery is presented in a set of interrelated
chapters that describe the various challenges and opportunities in the complementary areas that
make up cyberinfrastructure: computing systems,data, information resources, networking, digitally
enabled-sensors, instruments, virtual organizations, and observatories, along with an interoperable
suite of software services and tools."

Overview of OARnet Network

drupal — Mon, 13 Feb 2006 11:54:34 -0600

Presentation provides pictorial overview of Ohio Supercomputing Center's Third Frontier Network and it's relationship to Midwest regional networks.

Experiments with a Small Supercomputer

drupal — Wed, 18 May 2005 11:17:36 -0500

The authors find that with fail-over and load balancing software, these search clusters could become reliable enough for many services in the future.

3D Phone Calls: the Fusion of Virtual Reality, Networking, Supercomputing, and Data Mining

drupal — Tue, 21 Sep 2004 16:56:03 -0500

Scientists, engineers, artists and archeologists have fully embraced networked computer visualization as a means to discover, communicate and educate. They generate and access massive data sets that push the technology to its daily limits, using the web as a common interface portal to share. A logical extension to the two-dimensional computer/TV screen is virtual reality 3D visuals and sound, presented in a surround fashion that puts the user inside the data, able to explore freely by walking or flying around. Connecting these users with the right networks, supercomputers and data mining gives them a way to make 3D phone calls to each other. 3D Telephony, or Tele-Immersion as it is more commonly called, is quite complicated to achieve, and is very expensive. The virtual reality gear is $150,000 per station, the networks require Quality of Service or over-provisioning, the computing and data mining are high-end. 3D phone calls are truly a challenge to the Next Generation Internet: fortunately it's only a question of more bandwidth, silicon, display technology, and software to drive the cost way down. Unfortunately, the speed of light is too slow for many global applications, but this too, provides challenges to the community. This talk will describe ways to get this technology to the desktop, plug it into the National Technology Grid, address the deep local and global networking issues, and adapt to the needs of users.

Déjà Vu

drupal — Wed, 21 Apr 2004 12:19:51 -0500

The author communications system being developed the National LambdaRail initiative.

Bioinformatics: New Technology Models for Research, Education, and Service

drupal — Tue, 13 Apr 2004 09:25:27 -0500

Bioinformatics applies principles of information sciences and technologies to complex life sciences data. Life sciences research and education are increasingly dependent on bioinformatics and advanced information technologies in order to support their experimental approaches. This research bulletin provides an overview of the opportunities, challenges, and strategies for information technology organizations to optimally facilitate bioinformatics research and broader life sciences development efforts in higher education.

Super-Partnerships: Computational Science Curricula, High Performance Computing and the Professional Organizations

drupal — Fri, 22 Oct 1999 11:03:58 -0500

Since October 1997, NSF has supported two National Supercomputing Partnerships, led by the National Center for Supercomputing Applications (NCSA), University of Illinois at Urbana-Champaign, and the San Diego Supercomputer Center (SDSC), University of California, San Diego. The goal of this program is to create and maintain the national metacomputing environment, by supporting leading-edge technology and applications research, and promoting human, technological and administrative infrastructure for ubiquitous computing. This paper provides summaries of the individual presentations from the conference: (1) Building a faculty community to support curriculum development in computational science and engineering (Kris Stewart), (2) Repositories and Online Tools (Roscoe Giles), and (3) Sociology Workbench, an analytical interface to distributed resources for social scientists (Ilya Zaslavsky).