The idea that computational modeling and simulation represents a new branch of scientific methodology, alongside theory and experimentation, was introduced about two decades ago. It has since come to symbolize the enthusiasm and sense of importance that people in our community feel for the work they are doing. But when we try to assess how much progress we have made and where things stand along the developmental path for this new "third pillar of science," recalling some history about the development of the other pillars can help keep things in perspective. For example, we can trace the systematic use of experiments back to Galileo in the early 17th 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 experimental design and practice were finally analyzed and described in detail by R. A. Fisher and others in the first half of the 20th 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 general and widespread obsession with hardware is understandable, especially given exponential increases in processor performance, the constant evolution of processor architectures and supercomputer designs, and the natural fascination that people have for big, fast machines. 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 the complex software "ecosystem" it requires must take its place on the 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 process that starts with scientific ideas and ends with executable software, and 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 of Computational Science, with all 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 the performance 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 what Computational Science wants to become 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 or under funded or both.¹