Integrated Performance Monitoring
2014
National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory 26.1 Design and Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 26.2 Success Stories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 26.2.1 Chombo’s ftruncate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 26.2.2 MADBENCH and File System Health . . . . . . . . . . . . . . . . . . 302 26.2.3 Buffer Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 26.2.4 HPC Workload Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 26.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Application performance is usually tested as part of a benchmark or controlled characterization study. Conducted by tools experts, the applications are assessed in an “off-line” or non-production environment. In practice [6], the performance delivered by an application over the duration of many executions can vary in uncontrolled and significant ways. A properly load-balanced code and input deck can be easily, and often severely, bottle-necked by minor changes in input, concurrency, and execution environment. Many of these “minor” changes are too easy to arrive at through natural adjustments of scientific need or resource availability, which is the motivation behind the Integrated Performance Monitoring (IPM) framework to measure and improve in-vivo HPC application performance as it happens in a production computing setting.