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

Autor:	Davis, Joshua Hoke, Gao, Tao, Chandresekaran, Sunita, Taufer, Michela
Rok vydání:	2019
Předmět:	Computer Science - Distributed Parallel and Cluster Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Extended abstract submitted for the ACM Student Research Competition at SC18. 2nd place undergraduate poster
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.11694 Zobrazit plný text záznamu View this record from Arxiv