We describe our power profiling library called MonEQ, built on the IBM provided API.We integrate MonEQ into several benchmarks to show the data it produces.Applications have different power profiles based on usage of domains in node.There is no difference in power consumption in network for a given network topology.Scale can reduce power consumption but is far less important than execution time. The power consumption of state of the art supercomputers, because of their complexity and unpredictable workloads, is extremely difficult to estimate. Accurate and precise results, as are now possible with the latest generation of IBM Blue Gene/Q, are therefore a welcome addition to the landscape. Only recently have end users been afforded the ability to access the power consumption of their applications. However, just because it's possible for end users to obtain this data does not mean it's a trivial task. This emergence of new data is therefore not only understudied, but also not fully understood.In this paper, we describe our open source power profiling library called MonEQ, built on the IBM provided Environmental Monitoring (EMON) API. We show that it's lightweight, has extremely low overhead, is incredibly flexible, and has advanced features which end users can take advantage. We then integrate MonEQ into several benchmarks and show the data it produces and what analysis of this data can teach us. Going one step further we also describe how seemingly simple changes in scale or network topology can have dramatic effects on power consumption. To this end, previously well understood applications will now have new facets of potential analysis.
['Sean Wallace', 'Zhou Zhou', 'Venkatram Vishwanath', 'Susan Coghlan', 'John R. Tramm', 'Zhiling Lan', 'Michael E. Papka']
Application power profiling on IBM Blue Gene/Q