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Performance and energy efficiency analysis of HPC physics simulation applications in a cluster of ARM processors
Author(s) -
Bez Jean Luca,
Bernart Eliezer E.,
Santos Fernando F.,
Schnorr Lucas Mello,
Navaux Philippe Olivier Alexandre
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4014
Subject(s) - computer science , energy consumption , efficient energy use , compiler , supercomputer , computer cluster , energy (signal processing) , cluster (spacecraft) , power (physics) , parallel computing , embedded system , operating system , electrical engineering , statistics , physics , mathematics , quantum mechanics , engineering
Summary We analyze the feasibility and energy efficiency of using an unconventional cluster of low‐power Advanced RISC Machines processors to execute two scientific parallel applications. For this purpose, we have selected two applications that present high computational and communication cost: the Ondes3D that simulates geophysical events, and the all‐pairs N‐Body that simulates astrophysical events. We compare and discuss the impact of different compilation directives and processor frequency and how they interfere in Time‐to‐Solution and Energy‐to‐Solution . Our results demonstrate that by correctly tuning the application at compile time, for the Advanced RISC Machines architecture, we can considerably reduce the execution time and the energy spent by computing simulations. Furthermore, we observe reductions of up to 54.14% in Time‐to‐Solution and gains of up to 53.65% in Energy‐to‐Solution with two cores. Additionally, we consider the impact of two processor frequency governors on these metrics. Results indicate that the powersave governor presents a smaller instantaneous power consumption. However, it spends more time executing tasks, increasing the energy needed to achieve the solution. Finally, we correlate the energy consumption with the execution time in the experimental results using Pareto. These findings suggest that it is possible to explore low‐powered clusters for high‐performance computing applications by tuning application and hardware configuration to achieve energy efficiency. Copyright © 2016 John Wiley & Sons, Ltd.