PITAC’s Look at Computational Science
In June 2004, the President’s Information Technology Advisory Committee (PITAC) was charged by John Marburger, the President’s Science Adviser, to respond to seven questions regarding the state of computational science:
- How well is the Federal Government targeting the right research areas to support and enhance the value of computational science? Are agencies’ current priorities appropriate?
- How well is current Federal funding for computational science appropriately balanced between short term, low risk research and longer term, higher risk research? Within these research arenas, which areas have the greatest promise of contributing to breakthroughs in scientific research and inquiry?
- How well is current Federal funding balanced between fundamental advances in the underlying techniques of computational science versus the application of computational science to scientific and engineering domains? Which areas have the greatest promise of contributing to breakthroughs in scientific research and inquiry?
- How well are computational science training and research integrated with the scientific disciplines that are heavily dependent upon them to enhance scientific discovery? How should the integration of research and training among computer science, mathematical science, and the biological and physical sciences best be achieved to ensure the effective use of computational science methods and tools?
- How effectively do Federal agencies coordinate their support for computational science and its applications in order to maintain a balanced and comprehensive research and training portfolio?
- How well have Federal investments in computational science kept up with changes in the underlying computing environments and the ways in which research is conducted? Examples of these changes might include changes in computer architecture, the advent of distributed computing, the linking of data with simulation, and remote access to experimental facilities.
- What barriers hinder realizing the highest potential of computational science and how might these be eliminated or mitigated?
Since that time, I have chaired a PITAC subcommittee composed of Ruzena Bajcsy (UC-Berkeley), Manuel Fernandez (SI Ventures), José-Marie Griffiths (UNC-CH) and Randall Mott (Dell) to prepare a response to these questions. The subcommittee has also been assisted by two consultants, Chris Johnson (Utah) and Jack Dongarra (Tennessee). The subcommittee has solicited input at public meetings and held a Birds-of-a-Feather (BoF) Town Hall meeting at SC04 in November 2004.
Based on this input and extended discussions, the subcommittee has developed a working definition of computational science, which it is using to prepare a draft report. This definition, which is still in flux, attempts to recognize the interplay among algorithms and software, computer and information science and infrastructure:
Computational science is a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems. Computational science fuses three distinct elements: (a) algorithms(numerical and non-numerical) and modeling and simulation software developed to solve science (e.g., biological physical, and social), engineering and humanities problems; (b) computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components needed to solve computationally demanding problems; and (c) the computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science.
Computational science has several advantages over experimentation and theory. First, it often enables solution of problems more efficiently, more rapidly and less expensively. Second, it can solve problems computationally that otherwise could not be solved safely. Finally, it can solve problems whose solution is otherwise impossible (e.g., due to the inability to recreate experimental conditions).
The subcommittee has issued two interim working summaries, which are available on the web site of the National Coordination Office (NCO).1 These summaries contain draft findings and recommendations, which are still evolving. Preliminary findings, reported at the November PITAC meeting, include the following:
- Computing has become the third component of scientific discovery, complementing theory and experiment.
- The explosive growth in the resolution of sensors and scientific instruments has led to unprecedented volumes of experimental data. Computational science now broadly includes modeling, simulation and scenario assessment using sensor data from diverse sources.
- Complex multidisciplinary problems, from public policy through national security to scientific discovery and economic competitiveness, have emerged as new drivers of computational science, complementing the historical focus on single disciplines.
- Developing leading edge computational science applications is a complex process involving teams of people that must be sustained for a decade or more to yield the full fruits of investment.
- Short-term investment and limited strategic planning have led to excessive focus on incremental research rather than on the long-term research with lasting impact that can solve critical problems.
- Interdisciplinary education in computational science and computing technologies is inadequate, reflecting the traditional disciplinary boundaries in higher education. Only systemic change to university organizational structures will yield the needed outcomes.
- Computational science would benefit from a roadmap outlining decadal priorities for investment, with a clear assessment of those priorities derived from a survey of the problems and challenges. Agencies could then respond to these with a strategic plan in recognition of those priorities and funding requirements.
The subcommittee invites comments on responses to the charge, its preliminary findings and draft recommendations. Comments can be sent to email@example.com.