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In this poster, we quantitatively measure the impacts of data movement onperformance in MapReduce-based applications when executed on HPC systems. Weleverage the PAPI 'powercap' component to identify ideal conditions forexecution of our applications in terms of (1) dataset characteristics (i.e.,unique words); (2) HPC system (i.e., KNL and KNM); and (3) implementation ofthe MapReduce programming model (i.e., with or without combiner optimizations).Results confirm the high energy and runtime costs of data movement, and thebenefits of the combiner optimization on these costs.

Studying the Impact of Power Capping on MapReduce-based, Data-intensive Mini-applications on Intel KNL and KNM Architectures