The computational problems that scientists (and engineers) desire to solve are escalating to the point that both the programs they write and the computers they use to solve these problems are signi.cantly more complex than the familiar, well-understood sequential model on their desktops. While scientists could be trained to use emerging high-performance computing (HPC) models, it is much more effective to provide them with a higher-level programming environment that has been specialized to their particular domain. By coupling the HPC specialist and the domain scientists, Problem-Solving Environments (PSEs) provide a collaborative environment that allows scientists to focus on expressing their computational problem while the PSE and associated tools support mapping that domain-specific problem to a high-performance computing system.

In this paper, we describe Arches, an object-oriented framework for building domain-specific PSEs. The framework was designed to support a wide range of problem domains and to be extendable in a way that allows it to target very different high-performance computing models. To demonstrate this flexibility we describe two PSEs that have been developed from the same framework yet solve different problems and target very different computing platforms. The Coven PSE supports parallel applications that need the large-scale parallelism that is found in cost-effective Beowulf clusters. In contrast, the RCADE PSE targets reconfigurable computing (FPGA-based) platforms with fine-grain parallelism. RCADE was designed to aid NASA Earth Scientists interested in studying satellite instrument data and who are unlikely to be schooled in low-level hardware design.