The insights gained from this research on a particular code often lead to understandings about how to improve code performance. These insights are fed back to the science team to aid them in further code development. Results may also be useful to Silicon Graphics in improving their hardware and software systems, so results are often forwarded to the inhouse Silicon Graphics team and the inhouse computational scientists. Measurements of Interest Part of the research effort is to determine what aspects of science code structure and behavior have the greatest effect on performance. Timings and execution counts of interesting code segments.
Data flows between code segments.
Cache use issues. Storage allocation sizes and use profiles. Tools Used These studies use a variety of tools for instrumenting and measuring various characteristics of the science codes and their behavior. The Godiva software instrumentation tool, developed during FY97 and described below, remained stable in Version 3.4 during FY98. It served as the primary tool for understanding the behavior of ESS codes and systems in this project.