<span class="var-sub_title">Studying the Impact of Power Capping on MapReduce-Based, Data-Intensive Mini-Applications on Intel KNL and KNM Architectures</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Studying the Impact of Power Capping on MapReduce-Based, Data-Intensive Mini-Applications on Intel KNL and KNM Architectures


Student: Joshua H. Davis (University of Delaware)
Supervisor: Michela Taufer (University of Tennessee)

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

ACM-SRC Semi-Finalist: no

Poster: PDF
Poster Summary: pdf
Reproducibility Description Appendix: PDF


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