Investigating power capping toward energy‐efficient scientific applications. (22nd March 2018)

• Record Type: Journal Article
• Authors: Haidar, Azzam; Jagode, Heike; Vaccaro, Phil; YarKhan, Asim; Tomov, Stanimire; Dongarra, Jack
• Other Names: Ezzatti Pablo guestEditor.; Quintana‐Ortí Enrique S. guestEditor.; Remón Alfredo guestEditor.; Saak Jens guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 6(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-03-22
• Subjects: energy efficiency -- high performance computing -- Intel Xeon Phi -- Knights landing -- PAPI -- performance analysis -- performance counters -- power efficiency; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4485
• Languages: English
• ISSNs: 1532-0626

Abstract

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.