Mercury and freon: temperature emulation and management for server systems
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
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In this poster, an interactive offline power modeling and analysis tool based on the R programming language is described. Least squares regression coefficients are calculated with R scripts from Watt meter and proc file system data collected on a 20 compute node high performance computing (HPC) cluster. The High Performance Computing Challenge (HPCC) benchmark suite is used to represent "real world" HPC computations. Ten proc file system files are sampled for each benchmarked compute node. Results show power consumption of HPC applications can be modeled at the full cluster level using the proc file system both inexpensively and conveniently providing insight into the links between HPC cluster applications and cluster power consumption.