In June 2004, the President's Information Technology Advisory Committee (PITAC) was charged by John Marburger, the President's Science Advisory, to respond to seven questions regarding the state of computational science. Following over a year of hearings and deliberations, the committee released its report, entitled Computational Science: Ensuring America's Competitiveness, in June 2005. What follows are some of my personal perspectives on computational science, shaped by the committee experience. Any wild eyed, crazy ideas should be attributed to me, not to the committee.
Based on community input and extensive discussions, the PITAC computational science report1 included the following principal finding and recommendation.
Principal Finding. Computational science is now indispensable to the solution of complex problems in every sector, from traditional science and engineering domains to such key areas as national security, public health, and economic innovation. Advances in computing and connectivity make it possible to develop computational models and capture and analyze unprecedented amounts of experimental and observational data to address problems previously deemed intractable or beyond imagination. Yet, despite the great opportunities and needs, universities and the Federal government have not effectively recognized the strategic significance of computational science in either their organizational structures or their research and educational planning. These inadequacies compromise U.S. scientific leadership, economic competitiveness, and national security.
Succinctly, the principal finding highlights the emergence of computational science as the third pillar of scientific discovery, as a complement to theory and experiment. It also highlights the critical importance of computational science to innovation, security and scientific discovery, together with our failure to embrace computational science as a strategic, rather than a tactical capability. In many ways, computational science has been everyone’s “second priority,” rather than the unifying capability it could be.
Principal Recommendation. Universities and the Federal government’s R&D agencies must make coordinated, fundamental, structural changes that affirm the integral role of computational science in addressing the 21st century’s most important problems, which are predominantly multidisciplinary, multi-agency, multisector, and collaborative. To initiate the required transformation, the Federal government, in partnership with academia and industry, must also create and execute a multi-decade roadmap directing coordinated advances in computational science and its applications in science and engineering disciplines.
The principal recommendation emphasizes the silos and stovepipes (choose your favorite analogy) that separate disciplinary domains within computational science. There was widespread consensus from both those who testified and those on the committee that solving many of the most important problems of the 21st century will require integration of skills from diverse groups. The group also felt deeply that current organizational structures in academia and government placed limits on interdisciplinary education and research.
Based on this recognition, the committee’s principal recommendation was to create a long-term, regularly updated strategic roadmap of technologies (i.e., software, data management, architectures and systems, and programming and tools), application needs and their interplay. The long term, strategic aspect of this recommendation cannot be over-estimated. Many of our most important computational science challenges cannot be solved in 1-3 years. Nor is a series of three year plans the same as a 10-15 year plan.
Substantial, sustained investment, driven by multi-agency collaboration, is the only approach that will allow us to escape from our current technology quandary–high-performance computing systems that are based on fragile software and an excessive emphasis on peak performance, rather than sustained performance on important applications. Simply put, today’s computational science ecosystem is unbalanced, with a software and hardware base that is inadequate to keep pace with and support evolving application needs. By starving research in enabling software and hardware, the imbalance forces researchers to build atop crumbling and inadequate foundations. The result is greatly diminished productivity for both researchers and computing systems.
Similarly, we must embrace the data explosion from large-scale instruments and ubiquitous, microscale sensors–the personal petabyte is in sight! Given the strategic significance of this scientific trove, the Federal government must provide long-term support for computational science community data repositories. HPC cannot remain synonymous with computing, but must be defined broadly to include distributed sensors and storage.
In the 19th and 20th centuries, proximity to transportation systems (navigable rivers, seaports, railheads, and airports) was critical to success. Cities grew and developed around such transportation systems, providing jobs and social services. In today’s information economy, high-speed networking, data archives and computing systems play a similar role, connecting intellectual talent across geographic barriers via virtual organizations (VOs)–teams drawn from multiple organizations, with diverse skills and access to wide ranging resources, that can coordinate and leverage intellectual talent. Two examples serve to illustrate both the challenges and the opportunities that could accrue from visionary application of computational science.
Disaster Response. Hurricane Katrina drove home the centrality of VOs. In computational science terms, a rapid response VO would include integrated hurricane, storm surge, tornado spawning, environmental, transportation, communication and human dynamics models, together with the experts needed to analyze model outputs and shape public policy for evacuation, remediation and recovery. Computationally, solving such a complex problem requires real-time data fusion from wide arrays of distributed sensors, large and small; coupled, computational intense environmental models; and social behavior models. There are thousands of such 21st century problems, each awaiting application of computational science tools and techniques.
Systems Biology. The fusion of knowledge from genomics, protein structure, enzyme function and pathway and regulatory models to create systemic models of organelles, cells and organisms and their relation to the environment is one of the great biological challenges of the 21st century. By combining information from experiments, data gleaned from mining large-scale archives (e.g., genomic, proteomic, structural and other data), and large-scale biological simulations and computational models, we can gain insights into function and behavior–understanding life in a deep way. The time is near to mount a multidisciplinary effort to create artificial life, a computational counterpart to Craig Venter’s minimal genome project. Such an effort would combine engineering, genomics, proteomics and systems biology expertise, with profound implications for medicine and deep insights into biology.
The computational science opportunities have never been greater. It is time to act with vision and sustained commitment.
