Visit additional Tabor Communication Publications
October 28, 2005
This article originally appeared in the Summer 2005 issue of NCSA's Access magazine.
When we try to assess how much progress we have made in computational modeling and simulation, recalling some history about the related approaches of experiment and theory can help keep things in perspective. For example, we can trace the systematic use of experiment back to Galileo in the early seventeenth century. Yet for all the incredible successes it enjoyed over its first three centuries, the experimental method arguably did not fully mature until the elements of good design and practice were finally analyzed and described in detail by R. A. Fisher and others in the first half of the twentieth century. In that light, it seems clear that while computational science has had many remarkable youthful successes, it is still at a very early stage in its growth.
Many of us today who want to hasten that growth believe that the most progressive steps in that direction require much more community focus on the vital core of computational science: software and the mathematical models and algorithms it encodes. Of course the widespread obsession with hardware is understandable. No one who helps administer the TOP500 Supercomputer Sites project, as I do, can claim to be immune to it. But when it comes to advancing the cause of computational modeling and simulation as a new part of the scientific method, there is no doubt that its complex software ecosystem must take center stage.
At the application level the science has to be captured in mathematical models, which in turn are expressed algorithmically and ultimately encoded as software. Accordingly, on typical projects the majority of the funding goes to support this translation, which over its course requires intimate collaboration among domain scientists, computer scientists, and applied mathematicians. This process also relies on a large infrastructure of mathematical libraries, protocols, and system software that has taken years to build up and that must be maintained, ported, and enhanced for many years to come if the value of the application codes that depend on it are to be preserved and extended. The software that encapsulates all this time, energy, and thought routinely outlasts (usually by years, sometimes by decades) the hardware it was originally designed to run on, as well as the individuals who designed and developed it.
Thus the life of computational science revolves around a multifaceted software ecosystem. But today there is (and should be) a real concern that this ecosystem, including all of its complexities, is not ready for the major challenges that will soon confront the field. Domain scientists now want to create much larger, multi-dimensional applications in which a variety of previously independent models are coupled together, or even fully integrated. They hope to be able to run these applications on petascale systems with tens of thousands of processors, to extract all performance that these platforms can deliver, to recover automatically from the processor failures that regularly occur at this scale, and to do all this without sacrificing good programmability. This vision of computational science contains numerous unsolved and exciting problems for the software research community. Unfortunately, it also highlights aspects of the current software environment that are either immature, underfunded, or both, as Douglass Post and Lawrence Votta recently pointed out in Physics Today.
Advancing to the next stage of growth for computational simulation and modeling will require us to solve basic research problems in computer science and applied mathematics even as we create and promulgate a new paradigm for the development of scientific software. To make progress on both fronts simultaneously will require a level of sustained, interdisciplinary collaboration among the core research communities that, in the past, has only been achieved by forming and supporting research centers dedicated to such a common purpose. A stronger effort is needed by both government and the research community to embrace such a broader vision.
I believe that the time has come for the leaders of the computational science movement to focus their energies on creating such software research centers to carry out this indispensable part of the mission. The NCSA community has always been in the vanguard of efforts to catalyze and organize precisely these kinds of interdisciplinary research partnerships that we now require to transform the future of scientific software. I have every confidence that this community stands ready to step up again to this momentous new effort.
Jack Dongarra is university distinguished professor of computer science in the Computer Science Department at the University of Tennessee. He also holds the title of distinguished research staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory and is an adjunct professor in the Computer Science Department at Rice University. He specializes in numerical algorithms in linear algebra, parallel computing, use of advanced computer architectures, programming methodology, and tools for parallel computers. He is executive editor of the Cyberinfrastructure Technology Watch, a publication of the NSF-funded CyberInfrastructure Parntership.
May 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
May 22, 2013 |
At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
May 15, 2013 |
Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.