Characterization Of Power Usage And Performance In Data-Intensive Applications Using Mapreduce Over Mpi

This paper presents a quantitative evaluation of the power usage over time in data-intensive applications that use MapReduce over MPI. We leverage the PAPI powercap tool to identify ideal conditions for execution of our mini-applications in terms of (1) dataset characteristics (e.g., unique words in datasets); (2) system characteristics (e.g., KNL and KNM); and (3) implementation of the MapReduce programming model (e.g., impact of various optimizations). Results illustrate the high power utilization and runtime costs of data management on HPC architectures.