Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil; YarKhan, Asim; Tomov, Stanimire; Dongarra, Jack

doi:10.1002/cpe.4485

Title: Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

Journal Article · Sun Mar 25 00:00:00 EDT 2018 · Concurrency and Computation. Practice and Experience

DOI:https://doi.org/10.1002/cpe.4485· OSTI ID:1435180

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^[1]; Vaccaro, Phil ^[1]; YarKhan, Asim ^[1]; Tomov, Stanimire ^[1];

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Univ. of Tennessee, Knoxville, TN (United States). Innovative Computing Lab
Univ. of Tennessee, Knoxville, TN (United States). Innovative Computing Lab ; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Manchester (United Kingdom)

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. Lastly, 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.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1435180

Journal Information:: Concurrency and Computation. Practice and Experience, Vol. 31, Issue 6; ISSN 1532-0626

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 19 works

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References (20)

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Cited By (3)

Power-aware computing: Power-aware computing Ezzatti, Pablo; Quintana-Ortí, Enrique S.; Remón, Alfredo Concurrency and Computation: Practice and Experience, Vol. 31, Issue 6 https://doi.org/10.1002/cpe.5034	journal	November 2018
Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments Czarnul, Pawel; Proficz, Jerzy; Krzywaniak, Adam Scientific Programming, Vol. 2019 https://doi.org/10.1155/2019/8348791	journal	April 2019
Investigating power efficiency of mergesort Aljabri, Naif; Al-Hashimi, Muhammad; Saleh, Mostafa The Journal of Supercomputing, Vol. 75, Issue 10 https://doi.org/10.1007/s11227-019-02850-5	journal	April 2019

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