Evaluating the potential impact of HECC in this way necessitates a definition of high-end capability computing that differs from the usual platform-centric one. From the perspective of the scientists and engineers who are working to push the frontiers of knowledge in their fields, the computational capabilities they seek are those that enable new scientific insights. Those computational capabilities serve as a lever to pry new insight from a mass of data or a complicated mathematical model. The capabilities are not simply processing “muscle,” so they are not necessarily measured in terms of floating point operations per second or number of processors. Rather, from a researcher’s perspective, the high-end computational capabilities include whatever mix of hardware, models, algorithms, software, intellectual capacity, and computational infrastructure must be combined to enable the desired computations. High-end computing platforms are certainly part of that mix, but ambitious and progressive computational science and engineering is a systems process that depends on many factors. Thus the committee reached the following conclusion:

Conclusion 1. High-end capability computing (HECC) is advanced computing that pushes the bounds of what is computationally feasible. Because it requires a system of interdependent components and because the mix of critical-path elements varies from field to field, HECC should not be defined simply by the type of computing platform being used. It is nonroutine in the sense that it requires innovation and poses technology risks in addition to the risks normally associated with any research endeavor.

Infrastructure Needs of HECC

At the very least, HECC infrastructure consists of hardware, operating software, and applications software. There is also a need for data management tools, graphical interface tools, data analysis tools, and algorithms research and development. Some critical problem areas may need targeted research into mathematical models, and others might require training or incentives to speed a targeted community’s climb up the learning curve. All parts of the HECC ecosystem must be healthy in order for HECC-enabled research to thrive. Some high-opportunity fields will not fully exploit HECC unless other changes fall into place. An example is chemical separations: While there is already a strong community of computational chemists skilled at HECC, they are not generally working on problems of industrial importance, for a variety of valid reasons. If it is determined that scientific progress is impaired because of underexploitation of HECC, then the incentives that drive computational chemistry researchers should be changed.

Fields will generally take advantage of the increased availability of HECC in proportion to how much of the necessary infrastructure has been created—that is, whether the field is ready for HECC. All available evidence suggests that the advancement of science and its applications to society increasingly depends on computing. For all fields to contribute, they must receive support for whatever they need to ready them to capitalize on HECC. The committee members, even though very diverse in disciplinary and computing expertise, readily reached the following conclusion:

Conclusion 2. Advanced computational science and engineering is a complex enterprise that requires models, algorithms, software, hardware, facilities, education and training, and a community of researchers attuned to its special needs. Computational capabilities in different fields of science and engineering are limited in different ways, and each field will require a different set of investments before it can use HECC to overcome the field’s major challenges.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement