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Investigating power capping toward energy‐efficient scientific applications
Author(s) -
Haidar Azzam,
Jagode Heike,
Vaccaro Phil,
YarKhan Asim,
Tomov Stanimire,
Dongarra Jack
Publication year - 2018
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.4485
Subject(s) - petascale computing , computer science , efficient energy use , power (physics) , supercomputer , distributed computing , constraint (computer aided design) , set (abstract data type) , computer engineering , energy consumption , reliability engineering , computational science , parallel computing , electrical engineering , mechanical engineering , physics , quantum mechanics , programming language , engineering
Summary The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale‐level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. We quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.