The results are analyzed, and if more data is required, the instrumentation is modified and the code rerun. The insights gained from this research on a particular code often lead to understandings about how to improve the performance of the code. These insights are fed back to the science team to aid them in further development of the code. 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. Scaling with problem size and number of processors. Tools Used These studies use a variety of tools for instrumenting and measuring various characteristics of the science codes and their behavior. The primary tool to date has been the Godiva software system, developed by this project and described below.