The parallel scientific computing community is placing increasing emphasis on portability and scalability of programs, languages, and architectures. This creates new challenges for developers of parallel performance analysis tools, who will have to deal with increasing volumes of performance data drawn from diverse platforms. One way to meet this challenge is to incorporate sophisticated facilities for data interpretation and experiment planning within the tools themselves, giving them increased flexibility and autonomy in gathering and selecting performance data. This panel discussion brings together four research groups that have made advances in this direction.

The design process that spans the gap between the requirements acquisition process and the implementation process, in which the basic architecture of a system is defined, and functions are allocated to software, hardware, and human agents. is studied. The authors call this process composite system design. The goal is an interactive model of composite system design incorporating deficiency-driven design, formal analysis, incremental design and rationalization, and design reuse. They discuss knowledge representations and reasoning techniques that support these goals for the product (composite system) that they are designing, and for the design process. To evaluate the model, the authors report on its use to reconstruct the design of two existing composite systems rationally.>